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Sorry, we can’t lend you the apartment. These words from a potential lender made Rebecca's world came to a standstill. Before jumping to conclusions, let’s understand first what exactly happened in her case: Rebecca: So am I not allowed to rent an Apartment? Lender: You need to show us proof of income to be eligible for renting an apartment. Rebecca: Yeah, I’ve submitted a copy of my income tax returns. Lender: Sorry, we need a W-2 form or Pay Stubs. Rebecca: I am self-employed, I don’t get Pay Stubs. Lender: Sorry, we can’t lend you the apartment. Rebecca is an average-earning freelancer who was denied an apartment for an absurd reason. She did not have a W-2 form or a pay stub to show her income of proof. She had income tax returns and bank statements, but those were not enough to sanction her an apartment loan. Over around the world deal with this wrath almost daily in their lives. They are subjected to poor treatment, misjudged, and often denied services that they federally are entitled to. 60 million freelancers One such thing is approval for a personal loan, buying an expensive car or the apartment is another, and the third is health insurance. Forget buying, they sometimes aren’t allowed to rent an apartment in the absence of pay stubs. These are the grey areas where earning self-employed people face the heat and sometimes quit over excessive questions, legwork, and comprehensive paper trail. Image Source: Pixabay How to Show Proof of Income? Proof of income is imperative for freelancers as it helps them avail services reserved for all tax-paying citizens. However, freelancers are not employees who get monthly proof of income like pay stubs and W-2 forms, due to which they frequently face issues while buying or renting necessary amenities. This article features all the possible ways through which you can rent your dream apartment being a freelancer and not worry about the lack of employee benefits. Proof of income can be presented in the following ways: 1. Recent Tax Documents Tax documents are of great essence when you want to show someone how much money you make. However, there are limitations too. For instance, if it is your , then using tax documents as proof of earning won’t help much because you must show tax returns for the last 2-3 years. Even if we’re paying taxes as an employee till last year, it still won’t count much, as this doesn’t guarantee your earnings as a freelancer. first year of freelancing Another case of tax returns not working aligns with low tax returns. Freelancers, in general, tend to lower their taxes to avoid high taxable income. This gives a false sense of impression that the freelancers are making less money. 2 . Generate Pay Stubs Although you don't have a W-2 form as you don't have an employer. But you can issue a pay stub for the work you do. The prerequisite includes two bank accounts, one for your business and the other for your personal usage. Decide upon the salary you need to take out for your expenses. Afterward, you can issue a monthly salary and use a paystub generator for crafting a paystub. The above method is totally legal; speak out to any financial advisor for more clarity on the subject. Image Source: opploans What is a Pay Stub? Pay stubs, paycheck stubs, payslips - they come with different names, but the meaning is one. Every pay stub includes your name, employment number, work hours (objective), paid benefits, and your earnings for the month before and after the taxes. 3. Keeping Track of Invoices Invoices and contract copies serve as the secondary efforts regarding confirming proof of income. And secondary, they are not even entertained by all lenders or mortgage brokers. Always keep track of invoices for payments and contracts you deliver. Additionally, keep a note of the profit and loss statements too. Not only will this help you with income proof, but it will also help you to assess your business decisions better. 4. Bank Statements Bank statements are generally the last throw of dice; using them can prove your point, but it has drawbacks too. Bank slips can cause a loss of privacy concerning your personal expenses. It is better to use them as a last resort. Renting an Apartment Renting an apartment can be a cumbersome job for a freelancer. The prime reason is apparent. With no monthly trail of income coming in, landlords have a fair share of reservations about renting to freelancers. Image Source: Pexels Some of the common questions that dazzle landlords’ minds are: Whether they be able to pay rent on time or What happens if their work goes off after few months of the tenancy Who’ll be the guarantor considering they don’t have an employer The questions are generic, and we can’t blame the landlords for thinking on these lines. Lenders want to stay assured about who they are renting to and avoid choosing a financially unstable tenant. But there is hope, a bright one too. One good way of dealing with this lies in educating your potential client about your work profile. An upfront approach to clarify doubts fills confidence in the mind of the listener. Words merely won’t be enough, so show them your tax returns for the last 2-3 years. A bank statement for the previous 6 months will also improve your chances of getting a ‘Yes’ considerably. Also, if the need arises, don’t hesitate to ask them about involving a guarantor. For this, make sure you have everything in readiness before pitching for an apartment. Still, if nothing works out, try out a final resort by asking them to submit the security deposit in cash. Don’t forget to get a written receipt of it for the record. To Sum Up Working as a freelancer is challenging; there are no doubts about it. They are not assured of landing clients, timely payments, financial security, and even mental health. But as the world heads to being a Startup Jungle with millions of people , the future seems brighter. Hold on tight, and look for alternatives to seek simple solutions to obstacles. Keep believing in yourself; keep freelancing! preferring to be self-employed Featured Image Source: Unsplash

How Freelancers Can Solve Proof of Income Issue When Renting

In this article, we will go over a project on image recognition using Go. We will also create a Telegram bot, through which we can send images for recognition. The first thing we need is an already trained model. Yes, in this article we will not train our model. For this exercise, let's take a ready-made module from the docker image of ctava/tfcgo. To launch our project, we will need 4 terminals at the same time. In the first case, we will launch an image recognition server. In the second case, we will launch the bot. In the third case, we will launch a public tunnel for sending our bot "out". In the fourth - we will execute the command to register our bot. To start the recognition server, create a Dockerfile: FROM ctava/tfcgo

RUN mkdir -p /model && \
  curl -o /model/inception5h.zip -s && \
  unzip /model/inception5h.zip -d /model

WORKDIR / /src/imgrecognize
COPY src/ .
RUN build
ENTRYPOINT [ ]
EXPOSE "http://download.tensorflow.org/models/inception5h.zip" go go "/go/src/imgrecognize/imgrecognize" 8080 This way we will run our server in the image. Inside this image, we will have our server: src/imgrecognize. In addition, we will unpack the model in the directory: /model. For the server, the first thing we need is to set the value of the constant os.Setenv( , ) "TF_CPP_MIN_LOG_LEVEL" "2" This is necessary so as not to get an error: I tensorflow/core/platform/cpu_feature_guard.cc: ] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4 SSE4 AVX AVX2 FMA
unable to a tensor from image: Expected image (JPEG, PNG, or GIF), got empty file 140 .1 .2 make Here, we will not optimize our server, but simply run it through "ListenAndServe". On port 8080. Before starting the server, we will load our model (loadModel) and get our graph (modelGraph) and labels (labels). From the graph, which is stored in a file in the protobuf format "/model/tensorflow_inception_graph. pb". { model, err := ioutil.ReadFile(graphFile) err != { , , err
	}
	graph := tensorflow.NewGraph() err := graph.Import(model, ); err != { , , err
	} labelsFile, err := os.Open(labelsFile) err != { , , err
	} labelsFile.Close()
	scanner := bufio.NewScanner(labelsFile) labels [] scanner.Scan() {
		labels = (labels, scanner.Text())
	} graph, labels, scanner.Err()
} func loadModel () (*tensorflow.Graph, [] , error) string // Load inception model if nil return nil nil if "" nil return nil nil // Load labels if nil return nil nil defer var string for append return Actually, in "modelGraph" we keep the "structure" of our model and the key tools for working with it. And "labels" contains a "dictionary" for working with our model. Inside our HTTP handler, we are required to normalize the resulting image-normalizeImage. In order to pass it on to the recognition input in the future. To normalize, we convert our image from a Go value to a Tensor: tensor, err := tensorflow.NewTensor(buf.String()) After that, we get three variables graph, input, output, err := getNormalizedGraph() "graph" - we need to decode, resize, and normalize an image. The "input", together with the tensor, will be the "input point" for "communication" between our application and tensorflow. The "output" will be used as the output signal. Through "graph", we will also open a session to start normalization directly. session, err := tensorflow.NewSession(graph, ) nil Normalization Code: { buf bytes.Buffer
	_, err := io.Copy(&buf, imgBody) err != { , err
	}

	tensor, err := tensorflow.NewTensor(buf.String()) err != { , err
	}

	graph, input, output, err := getNormalizedGraph() err != { , err
	}

	session, err := tensorflow.NewSession(graph, ) err != { , err
	}

	normalized, err := session.Run( [tensorflow.Output]*tensorflow.Tensor{
			input: tensor,
		},
		[]tensorflow.Output{
			output,
		}, ) err != { , err
	} normalized[ ], } func normalizeImage (imgBody io.ReadCloser) (*tensorflow.Tensor, error) var if nil return nil if nil return nil if nil return nil nil if nil return nil map nil if nil return nil return 0 nil After normalizing the image, we create a session for inference over modelGraph. session, err := tensorflow.NewSession(modelGraph, ) nil With the help of this session (session), we will start the recognition itself. The input is our normalized image modelGraph.Operation( ).Output( ): normalizedImg, "input" 0 The result of the calculation (recognition) will be saved in the "outputRecognize"variable. From the received data we get the last 3 results: res := getTopFiveLabels(labels, outputRecognize[ ].Value().([][] )[ ]) { resultLabels []Label i, p := probabilities { i >= (labels) { }
		resultLabels = (resultLabels, Label{Label: labels[i], Probability: p})
	}
	sort.Sort(Labels(resultLabels)) resultLabels[:ResultCount]
} 0 float32 0 [] func getTopFiveLabels (labels [] , probabilities [] ) string float32 Label var for range if len break append return And for the HTTP response, we will give only one most likely result: msg := fmt.Sprintf( , res[ ].Label, res[ ].Probability* )
_, err = w.Write([] (msg)) "This is: %s (%.2f%%)" 0 0 100 byte All the code of our server for recognition: main ( tensorflow ) (
	ResultCount = ) (
	graphFile  = labelsFile = ) Label {
	Label Probability } Labels []Label { (l)
} {
	l[i], l[j] = l[j], l[i]
} { l[i].Probability > l[j].Probability
} (
	modelGraph *tensorflow.Graph
	labels     [] ) { err := os.Setenv( , ) err != {
		log.Fatalln(err)
	}

	modelGraph, labels, err = loadModel() err != {
		log.Fatalf( , err)
	}

	log.Println( )
	http.HandleFunc( , mainHandler)
	err = http.ListenAndServe( , ) err != {
		log.Fatalln(err)
	}
} {
	normalizedImg, err := normalizeImage(r.Body) err != {
		log.Fatalf( , err)
	} session, err := tensorflow.NewSession(modelGraph, ) err != {
		log.Fatalf( , err)
	}

	outputRecognize, err := session.Run( [tensorflow.Output]*tensorflow.Tensor{
			modelGraph.Operation( ).Output( ): normalizedImg,
		},
		[]tensorflow.Output{
			modelGraph.Operation( ).Output( ),
		}, ,
	) err != {
		log.Fatalf( , err)
	}

	res := getTopFiveLabels(labels, outputRecognize[ ].Value().([][] )[ ])
	log.Println( ) _, l := res {
		fmt.Printf( , l.Label, l.Probability* )
	}
	log.Println( )

	msg := fmt.Sprintf( , res[ ].Label, res[ ].Probability* )
	_, err = w.Write([] (msg)) err != {
		log.Fatalf( , err)
	}
} { model, err := ioutil.ReadFile(graphFile) err != { , , err
	}
	graph := tensorflow.NewGraph() err := graph.Import(model, ); err != { , , err
	} labelsFile, err := os.Open(labelsFile) err != { , , err
	} labelsFile.Close()
	scanner := bufio.NewScanner(labelsFile) labels [] scanner.Scan() {
		labels = (labels, scanner.Text())
	} graph, labels, scanner.Err()
} { resultLabels []Label i, p := probabilities { i >= (labels) { }
		resultLabels = (resultLabels, Label{Label: labels[i], Probability: p})
	}
	sort.Sort(Labels(resultLabels)) resultLabels[:ResultCount]
} { buf bytes.Buffer
	_, err := io.Copy(&buf, imgBody) err != { , err
	}

	tensor, err := tensorflow.NewTensor(buf.String()) err != { , err
	}

	graph, input, output, err := getNormalizedGraph() err != { , err
	}

	session, err := tensorflow.NewSession(graph, ) err != { , err
	}

	normalized, err := session.Run( [tensorflow.Output]*tensorflow.Tensor{
			input: tensor,
		},
		[]tensorflow.Output{
			output,
		}, ) err != { , err
	} normalized[ ], } {
	s := op.NewScope()
	input = op.Placeholder(s, tensorflow.String)
	decode := op.DecodeJpeg(s, input, op.DecodeJpegChannels( )) output = op.Sub(s,
		op.ResizeBilinear(s,
			op.ExpandDims(s,
				op.Cast(s, decode, tensorflow.Float),
				op.Const(s.SubScope( ), ( ))),
			op.Const(s.SubScope( ), [] { , })),
		op.Const(s.SubScope( ), ( )))
	graph, err = s.Finalize() graph, input, output, err
} package import "bufio" "bytes" "fmt" "io" "io/ioutil" "log" "net/http" "os" "sort" "github.com/tensorflow/tensorflow/tensorflow/go" "github.com/tensorflow/tensorflow/tensorflow/go/op" const 3 var "/model/tensorflow_inception_graph.pb" "/model/imagenet_comp_graph_label_strings.txt" type struct string float32 type func (l Labels) Len () int return len func (l Labels) Swap (i, j ) int func (l Labels) Less (i, j ) int bool return var string func main () // I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA // unable to make a tensor from image: Expected image (JPEG, PNG, or GIF), got empty file "TF_CPP_MIN_LOG_LEVEL" "2" if nil if nil "unable to load model: %v" "Run RECOGNITION server ...." "/" ":8080" nil if nil func mainHandler (w http.ResponseWriter, r *http.Request) if nil "unable to make a normalizedImg from image: %v" // Create a session for inference over modelGraph nil if nil "could not init session: %v" map "input" 0 "output" 0 nil if nil "could not run inference: %v" 0 float32 0 "--- recognition result:" for range "label: %s, probability: %.2f%%\n" 100 "---" "This is: %s (%.2f%%)" 0 0 100 byte if nil "could not write server response: %v" func loadModel () (*tensorflow.Graph, [] , error) string // Load inception model if nil return nil nil if "" nil return nil nil // Load labels if nil return nil nil defer var string for append return [] func getTopFiveLabels (labels [] , probabilities [] ) string float32 Label var for range if len break append return func normalizeImage (imgBody io.ReadCloser) (*tensorflow.Tensor, error) var if nil return nil if nil return nil if nil return nil nil if nil return nil map nil if nil return nil return 0 nil // Creates a graph to decode, rezise and normalize an image func getNormalizedGraph () (graph *tensorflow.Graph, input, output tensorflow.Output, err error) 3 // 3 RGB "make_batch" int32 0 "size" int32 224 224 "mean" float32 117 return Now, we need to build this image (build it). Of course, we can build an image and run it in the console using the appropriate commands. But it is more convenient to build these commands in a Makefile. So, let's create this handy file: recognition_build:
	docker build -t imgrecognition .

recognition_run:
	docker run -it -p : imgrecognition 8080 8080 After that, open the terminal and run the command: recognition_build && recognition_run make make Now, in the first terminal, we have a local HTTP server that can accept images. In response, it sends a text message containing information about what was recognized in the image. This is so to say the "core" of our project. Creating a Telegram Bot Next, we need to create a Telegram bot. We need to "build" the bot; to do this, we need to write a second HTTP server. The first server recognizes our images and uses port 8080. The second one will be the Bot's server and will use port 3000. First, we need to create a bot through your account in the app via . With this registration, you will receive the bot's name and its token. Don't tell anyone about this token. BotFather Let's put this token in the "BotToken" constant. You should get something like this: BotToken = const "1695571234:AAEbodyrfOjto2xNE5yjpQpW2Gyq0Ob5X24D5" Our bot's handler will decode the JSON response body. json.NewDecoder(r.Body).Decode(webhookBody) We are interested in the photo in the sent message By the unique image ID- let's collect a link to the image itself . And download it webhookBody.Message.Photo. photoSize.FileID fmt.Sprintf(GetFileUrl, BotToken, photoSize.FileID) downloadResponse, err = http.Get(downloadFileUrl). We will send the image bytes to the handler of our first server: msg := recognitionClient.Recognize(downloadResponse) In response, we get a certain message - a text string. After that, we simply send this string to the User, as is, in the Telegram Bot. The entire bot code: main ( ) (
	BotToken = GetFileUrl       = DownloadFileUrl  = SendMsgToUserUrl = ) webhookReqBody {
	Message Msg
} Msg {
	MessageId Text From {
		ID FirstName Username } Photo *[]PhotoSize Chat {
		ID FirstName Username } Date Voice {
		Duration MimeType FileId FileSize } } PhotoSize {
	FileID Width Height FileSize } ImgFileInfo {
	Ok Result {
		FileId FileUniqueId FileSize FilePath } } {
	log.Println( )
	err := http.ListenAndServe( , http.HandlerFunc(Handler)) err != {
		log.Fatalln(err)
	}
} { webhookBody := &webhookReqBody{}
	err := json.NewDecoder(r.Body).Decode(webhookBody) err != {
		log.Println( , err) } downloadResponse *http.Response webhookBody.Message.Photo == {
		log.Println( , webhookBody) } _, photoSize := *webhookBody.Message.Photo { imgFileInfoUrl := fmt.Sprintf(GetFileUrl, BotToken, photoSize.FileID)
		rr, err := http.Get(imgFileInfoUrl) err != {
			log.Println( , err) } rr.Body.Close() fileInfoJson, err := ioutil.ReadAll(rr.Body) err != {
			log.Println( , err) } imgInfo := &ImgFileInfo{}
		err = json.Unmarshal(fileInfoJson, imgInfo) err != {
			log.Println( +imgFileInfoUrl, err)
		} downloadFileUrl := fmt.Sprintf(DownloadFileUrl, BotToken, imgInfo.Result.FilePath)
		downloadResponse, err = http.Get(downloadFileUrl) err != {
			log.Println( +downloadFileUrl, err) } downloadResponse.Body.Close()
	} recognitionClient := recognition.New()
	msg := recognitionClient.Recognize(downloadResponse) err := sendResponseToUser(webhookBody.Message.Chat.ID, msg); err != {
		log.Println( , err) }
} sendMessageReqBody {
	ChatID Text } { msgBody := &sendMessageReqBody{
		ChatID: chatID,
		Text:   msg,
	} msgBytes, err := json.Marshal(msgBody) err != { err
	} res, err := http.Post(fmt.Sprintf(SendMsgToUserUrl, BotToken), , bytes.NewBuffer(msgBytes)) err != { err
	} res.StatusCode != http.StatusOK {
		buf := (bytes.Buffer)
		_, err := buf.ReadFrom(res.Body) err != { err
		} errors.New( + res.Status)
	} } package import "bytes" "encoding/json" "errors" "fmt" "io/ioutil" "log" "net/http" "github.com/romanitalian/recognition/src/bot/recognition" // Register Bot: curl -F "url=https://9068b6869da7.ngrok.io "  https://api.telegram.org/bot1695571234:AAEbodyrfOjto2xNE5yjpQpW2Gyq0Ob5X24D5/setWebhook const "1695571234:AAEbodyrfOjto2xNE5yjpQpW2Gyq0Ob5X24D5" "https://api.telegram.org/bot%s/getFile?file_id=%s" "https://api.telegram.org/file/bot%s/%s" "https://api.telegram.org/bot%s/sendMessage" type struct type struct int `json:"message_id"` string `json:"text"` struct int64 `json:"id"` string `json:"first_name"` string `json:"username"` `json:"from"` `json:"photo"` struct int64 `json:"id"` string `json:"first_name"` string `json:"username"` `json:"chat"` int `json:"date"` struct int64 `json:"duration"` string `json:"mime_type"` string `json:"file_id"` int64 `json:"file_size"` `json:"voice"` type struct string `json:"file_id"` int `json:"width"` int64 `json:"height"` int64 `json:"file_size"` type struct bool `json:"ok"` struct string `json:"file_id"` string `json:"file_unique_id"` int `json:"file_size"` string `json:"file_path"` `json:"result"` func main () "Run BOT server ...." ":3000" if nil // This handler is called everytime telegram sends us a webhook event func Handler (w http.ResponseWriter, r *http.Request) // First, decode the JSON response body if nil "could not decode request body" return // ------------------------- Download last img var if nil "no photo in webhook body. webhookBody: " return for range // GET JSON ABOUT OUR IMG (ORDER TO GET FILE_PATH) if nil "unable retrieve img by FileID" return defer // READ JSON if nil "unable read img by FileID" return // UNMARSHAL JSON if nil "unable unmarshal file description from api.telegram by url: " // GET FILE_PATH if nil "unable download file by file_path: " return defer // --------------------------- Send img to server recognition. if nil "error in sending reply: " return // The below code deals with the process of sending a response message // to the user // Create a struct to conform to the JSON body // of the send message request // https://core.telegram.org/bots/api#sendmessage type struct int64 `json:"chat_id"` string `json:"text"` // sendResponseToUser notify user - what found on image. func sendResponseToUser (chatID , msg ) int64 string error // Create the request body struct // Create the JSON body from the struct if nil return // Send a post request with your token "application/json" if nil return if new if nil return return "unexpected status: " return nil The client code that sends the image request from the Bot to the Recognition Server: recognition ( ) imgRecognitionAddress = Client {
	httpClient *http.Client
} { &Client{
		httpClient: &http.Client{},
	}
} { msg method := req, err := http.NewRequest(method, imgRecognitionAddress, downloadResponse.Body) err != {
		log.Println( , err) msg
	}
	req.Header.Add( , ) recognitionResponse, err := c.httpClient.Do(req) err != {
		log.Println(err) msg
	} {
		er := recognitionResponse.Body.Close() er != {
			log.Println(er)
		}
	}()

	recognitionResponseBody, err := ioutil.ReadAll(recognitionResponse.Body) err != {
		log.Println( , err) msg
	}
	msg = (recognitionResponseBody) msg
} package import "io/ioutil" "log" "net/http" const "http://localhost:8080/" type struct * func New () Client return func (c *Client) Recognize (downloadResponse *http.Response) string var string "POST" if nil "error from server recognition" return "Content-Type" "image/png" // do request to server recognition. if nil return defer func () if nil if nil "error on read response from server recognition" return string return By the way, to make our bot work correctly-register our handler. To do this, run: ngrok http 3000 Immediately after executing this command, you will see a list of public addresses. The last one will be an address with HTTPS - we need it. For example, it can be: https://9068b6869da7.ngrok.io. And directly register our bot-say Telegram where to send webhooks: curl -F https: "url=https://9068b6869da7.ngrok.io" //api.telegram.org/bot1695571234:AAEbodyrfOjto2xNE5yjpQpW2Gyq0Ob5X24D5/setWebhook Now you can send a file with a photo to your bot and get information about what is depicted on it. Thanks for your attention.

Creating an Image Recognizer on Golang Telegram Bot

Futurist Scott Smith: «The next Uber is going to be a biotech-company.»

FRANKFURT, Germany, November 20th, 2024/Chainwire/--KYVE is entering its expansion era, introducing an enhanced web app packed with new features and collaborations to support users and unlock multi-network potential. From experienced developers to crypto newcomers, the platform aims to make it easier for users to jump in, contribute, and benefit as part of KYVE's mission to make blockchain data accessible to all. The latest updates are meant to make it easier for users of all levels to engage with KYVE’s ecosystem, fostering cross-network collaboration and data support across diverse blockchain platforms. What’s New? Staking and Participation in KYVE’s Network: The KYVE web app now features an in-app guide designed to facilitate staking of $KYVE. Participants may receive rewards in $KYVE as well as in other tokens integrated into the KYVE protocol. This feature supports a diverse range of opportunities for stakers and validators, contributing to broader participation in the KYVE ecosystem.
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About KYVE KYVE Network is a decentralized data management solution that provides specialized tools for data archiving, validating, and accessing blockchain data. KYVE's protocol ensures that only accurate historical data from a blockchain is made immutable and easily accessible for all. As a result, KYVE allows other chains to decentralize their historical data and overall data accessibility management and provides essential tooling to access this data, enabling enhanced scalability of chains and ecosystem development. KYVE is one of the most supported blockchains in the space, backed by Arweave, Ava Labs, Solana Foundation, Interchain Foundation, Moonbeam, TheGraph, Parity Technologies, Composable Finance, Zilliqa, Mina Foundation, Aurora, and NEAR Foundation. As well as VCs such as Hypersphere Ventures, Coinbase Ventures, Distributed Global, Mechanism Capital, CMS Holdings, IOSG Ventures, and others.‍ Contact Head of Marketing Margaux Stancil KYVE margaux@kyve.network This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here FRANKFURT, Germany, November 20th, 2024/Chainwire/--KYVE is entering its expansion era, introducing an enhanced web app packed with new features and collaborations to support users and unlock multi-network potential. From experienced developers to crypto newcomers, the platform aims to make it easier for users to jump in, contribute, and benefit as part of KYVE's mission to make blockchain data accessible to all. FRANKFURT, Germany, November 20th, 2024/Chainwire/--KYVE The latest updates are meant to make it easier for users of all levels to engage with KYVE’s ecosystem , fostering cross-network collaboration and data support across diverse blockchain platforms. engage with KYVE’s ecosystem engage with KYVE’s ecosystem What’s New? Staking and Participation in KYVE’s Network: The KYVE web app now features an in-app guide designed to facilitate staking of $KYVE. Participants may receive rewards in $KYVE as well as in other tokens integrated into the KYVE protocol. This feature supports a diverse range of opportunities for stakers and validators, contributing to broader participation in the KYVE ecosystem. What is backing this? KYVE’s multi-coin funding feature and Public Goods Funding Program already partnered with SOURCE Protocol, Andromeda, a dYdX grant, Lava Network, and others to come. Staking and Participation in KYVE’s Network: The KYVE web app now features an in-app guide designed to facilitate staking of $KYVE. Participants may receive rewards in $KYVE as well as in other tokens integrated into the KYVE protocol. This feature supports a diverse range of opportunities for stakers and validators, contributing to broader participation in the KYVE ecosystem. What is backing this? KYVE’s multi-coin funding feature and Public Goods Funding Program already partnered with SOURCE Protocol, Andromeda, a dYdX grant, Lava Network, and others to come. multi-coin funding multi-coin funding Public Goods Funding Program Public Goods Funding Program “We're proud to support KYVE Network in permanently archiving all historical dYdX data and further streamlining data accessibility for ecosystem participants!” – dYdX Grants on X. “We're proud to support KYVE Network in permanently archiving all historical dYdX data and further streamlining data accessibility for ecosystem participants!” – dYdX Grants on X. dYdX Grants on X. dYdX Grants on X. __ https://www.youtube.com/embed/LpdwF8RVJnA __ https://www.youtube.com/embed/LpdwF8RVJnA Testing with Mainnet Faucets: KYVE’s newly introduced faucets allow users to test the platform’s features in a low-commitment environment. Whether they're exploring staking or running a validator, these faucets are designed to make it easier for new users to experiment and get comfortable before fully committing. Testing with Mainnet Faucets: KYVE’s newly introduced faucets allow users to test the platform’s features in a low-commitment environment. Whether they're exploring staking or running a validator, these faucets are designed to make it easier for new users to experiment and get comfortable before fully committing. faucets faucets One-Stop Access with Kado and Skip: Integrated directly into the KYVE web app, Kado and Skip simplify the process of swapping or purchasing $KYVE, removing entry barriers and making it easier for users to start participating in KYVE’s network. One-Stop Access with Kado and Skip: Integrated directly into the KYVE web app, Kado and Skip simplify the process of swapping or purchasing $KYVE, removing entry barriers and making it easier for users to start participating in KYVE’s network. Kado and Skip Kado and Skip What This Means for Users With these new features, KYVE is unlocking a wide range of opportunities for users to support the future of blockchain data scalability while earning rewards across multiple networks. The updated web app is designed to accommodate both experienced blockchain developers and newcomers, KYVE’s new web app enhancements are designed to support contributions to the platform’s mission of preserving historical chain data while providing value to participants. As KYVE expands its solutions to more ecosystems, the focus remains on providing streamlined access, empowering users to play an active role in supporting blockchain scalability and data preservation. KYVE is dedicated to ensuring that all blockchains receive the data support they need to thrive and that all types of users can take part in this mission. About KYVE KYVE Network is a decentralized data management solution that provides specialized tools for data archiving, validating, and accessing blockchain data. KYVE's protocol ensures that only accurate historical data from a blockchain is made immutable and easily accessible for all. KYVE KYVE As a result, KYVE allows other chains to decentralize their historical data and overall data accessibility management and provides essential tooling to access this data, enabling enhanced scalability of chains and ecosystem development. KYVE is one of the most supported blockchains in the space, backed by Arweave, Ava Labs, Solana Foundation, Interchain Foundation, Moonbeam, TheGraph, Parity Technologies, Composable Finance, Zilliqa, Mina Foundation, Aurora, and NEAR Foundation. As well as VCs such as Hypersphere Ventures, Coinbase Ventures, Distributed Global, Mechanism Capital, CMS Holdings, IOSG Ventures, and others.‍ Contact Head of Marketing Margaux Stancil KYVE margaux@kyve.network This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Fueling KYVE’s Expansion Era: A New Age Of Interoperable Data Opportunities For All

KYVE’s Expansion Era ukar ch’amanchaña: Mä Machaq pacha Interoperable Datos Oportunidades Taqinitak

Падсілкоўванне эры пашырэння KYVE: новая эра сумяшчальных магчымасцей перадачы дадзеных для ўсіх

Fueling KYVE's Expansion Era: En ny tidsalder med interoperable datamuligheder for alle

Impulsando la era de expansión de KYVE: una nueva era de oportunidades de datos interoperables para todos

KYVEren Hedapen Aroa Elikatuz: Guztientzako Datu Interoperagarrien Aukeren Aro Berria

Impulsando a era de expansión de KYVE: unha nova era de oportunidades de datos interoperables para todos

מתדלק את עידן ההתרחבות של KYVE: עידן חדש של הזדמנויות נתונים הדדיות לכולם

KYVE の拡大時代を推進: 相互運用可能なデータ機会をすべての人に提供する新時代

ជំរុញយុគសម័យពង្រីករបស់ KYVE៖ យុគសម័យថ្មីនៃឱកាសទិន្នន័យដែលអាចធ្វើអន្តរកម្មសម្រាប់ទាំងអស់គ្នា

Fueling KYVE:s Expansion Era: En ny tid av interoperabla datamöjligheter för alla

Давраи васеъшавии KYVE-ро пурзӯр мегардонад: асри нави имкониятҳои муштараки маълумот барои ҳама

ขับเคลื่อนยุคการขยายตัวของ KYVE: ยุคใหม่ของโอกาสด้านข้อมูลที่สามารถทำงานร่วมกันได้สำหรับทุกคน

Discovering the Opportunities and Threats of MiCA Regulations on Crypto Dark Pools in the EU 

TORONTO, Ontario, October 8th, 2024/Chainwire/--clisBNB, developed by Lista DAO, is an innovative tool that offers BNB holders greater flexibility and control over their assets. Now available through BNB.xyz, a one-stop on-chain platform for BNB, primarily designed to bridge the gap between CeFi and DeFi. clisBNB offers BNB holders a new way to access Binance launchpool and Lista DAO CDP. What is BNB.xyz? With BNB Chain, ListaDAO, and Pancakeswap as core contributors, BNB.XYZ is a one-stop staking platform on the BNB chain, bridging the gap between DeFi and CeFi. The platform offers users competitive APRs, enhanced security, and exclusive features like strategic APY comparisons, Binance Launchpool reminders, and potential integrations with major platforms like Binance.com. What is clisBNB? clisBNB is a certificate token issued when users deposit BNB into a Collateralized Debt Position (CDP) on ListaDAO. Each clisBNB is pegged 1:1 to the amount of BNB deposited, maintaining a clear link between the token and the staked BNB. This innovative solution allows users to retain control of their BNB collateral while accessing a wide range of DeFi opportunities. What Can clisBNB Do? The primary function of clisBNB is to allow users to borrow lisUSD against their BNB collateral on ListaDAO, and still be able to participate in Binance launchpool using clisBNB. Users who hold clisBNB in their Binance Web3 MPC wallet can leverage their BNB to join exclusive token launch events, earning new tokens without having to close their debt positions on Lista DAO. This powerful combination of borrowing and participation in Binance Launchpool gives users more utility and flexibility with their BNB holdings, all through one seamless product. Key Features of clisBNB: clisBNB comes with several features that distinguishes it from other tokens, providing users with focused utility while maintaining control over their assets: 1:1 Ratio with BNB
When users deposit BNB into a CDP on Lista DAO, they are issued clisBNB at a 1:1 ratio. This direct relationship ensures that for every BNB deposited, one clisBNB is created, maintaining a seamless link between the deposit and the certificate.
Non-transferable Token
One important feature of clisBNB is that it is non-transferable. Once issued, clisBNB cannot be moved between wallets or users. It is solely linked to the user’s BNB deposit and exists only within the context of the Lista DAO platform, ensuring its security and focused usage.
Automatic burn upon withdrawal
clisBNB is automatically burnt or destroyed when users withdraw their BNB or the corresponding slisBNB from Lista DAO’s CDP. This mechanism ensures that clisBNB always accurately represents the amount of BNB in the user’s position. Once the underlying BNB is withdrawn, the associated clisBNB is removed from the system to prevent any mismatches in value. Minting clisBNB to Another Address clisBNB offers users a unique feature: When depositing BNB into a CDP, they have the option to mint clisBNB to another address. However, once this address has been chosen and the clisBNB has been sent over, it will no longer be transferable to any other wallet. Having this function enables greater flexibility, especially for users who may want to delegate certain actions related to another wallet, such as the accumulation of stardust points. However, this flexibility comes with some key restrictions: Only one address can be specified to receive clisBNB at the time of deposit. If the underlying BNB or slisBNB is withdrawn, any clisBNB minted to the secondary address is automatically destroyed. How to Obtain clisBNB Accessing clisBNB is a straightforward process. Users can visit ListaDAO’s deposit page, where they can deposit their BNB into a Collateralized Debt Position (CDP), and automatically receive clisBNB in return. From there, clisBNB can be used to participate in Binance Launchpool events via the Binance Web3 MPC wallet. To ensure participation in Binance Launchpool events, it’s important that users utilize the Binance Web3 MPC wallet, which supports smooth interaction between the staked BNB and launch events. clisBNB: Unlocking BNB Liquidity clisBNB offers BNB holders a powerful new way to unlock liquidity, borrow lisUSD, and participate in Binance Launchpool events without losing access to their collateral. By enhancing the utility and flexibility of staked BNB, clisBNB represents a significant advancement in DeFi solutions for BNB users. With the introduction of clisBNB, BNB holders now have a seamless tool for managing collateral, participating in Binance launchpools and accessing DeFi opportunities, all within a simple and secure process. About BNB XYZ BNB XYZ is a One-Stop on Chain Staking Platform for BNB, designed to bridge the gap between BNB CeFi and DeFi. It offers seamless staking solutions and optimizes the utility of both CeFi BNB and DeFi BNB, making it the go-to resource for all BNB users to maximize their potential earnings. Core contributors of BNB XYZ include PancakeSwap, Lista DAO, and BNB Chain. Website: https://BNB.xyz
X (Twitter): https://x.com/BNBxyz Core contributors: PancakeSwap, Lista DAO, BNB chain Contact Marketing team
BNB.xyz
marketing@bnb.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here TORONTO, Ontario, October 8th, 2024/Chainwire/--clisBNB, developed by Lista DAO, is an innovative tool that offers BNB holders greater flexibility and control over their assets. Now available through BNB.xyz, a one-stop on-chain platform for BNB, primarily designed to bridge the gap between CeFi and DeFi. clisBNB offers BNB holders a new way to access Binance launchpool and Lista DAO CDP. What is BNB.xyz? With BNB Chain, ListaDAO, and Pancakeswap as core contributors, BNB.XYZ is a one-stop staking platform on the BNB chain, bridging the gap between DeFi and CeFi. The platform offers users competitive APRs, enhanced security, and exclusive features like strategic APY comparisons, Binance Launchpool reminders, and potential integrations with major platforms like Binance.com . Binance.com What is clisBNB? clisBNB is a certificate token issued when users deposit BNB into a Collateralized Debt Position (CDP) on ListaDAO. Each clisBNB is pegged 1:1 to the amount of BNB deposited, maintaining a clear link between the token and the staked BNB. This innovative solution allows users to retain control of their BNB collateral while accessing a wide range of DeFi opportunities. What Can clisBNB Do? The primary function of clisBNB is to allow users to borrow lisUSD against their BNB collateral on ListaDAO, and still be able to participate in Binance launchpool using clisBNB. Users who hold clisBNB in their Binance Web3 MPC wallet can leverage their BNB to join exclusive token launch events, earning new tokens without having to close their debt positions on Lista DAO. This powerful combination of borrowing and participation in Binance Launchpool gives users more utility and flexibility with their BNB holdings, all through one seamless product. Key Features of clisBNB: clisBNB comes with several features that distinguishes it from other tokens, providing users with focused utility while maintaining control over their assets: 1:1 Ratio with BNB
When users deposit BNB into a CDP on Lista DAO, they are issued clisBNB at a 1:1 ratio. This direct relationship ensures that for every BNB deposited, one clisBNB is created, maintaining a seamless link between the deposit and the certificate. Non-transferable Token
One important feature of clisBNB is that it is non-transferable. Once issued, clisBNB cannot be moved between wallets or users. It is solely linked to the user’s BNB deposit and exists only within the context of the Lista DAO platform, ensuring its security and focused usage. Automatic burn upon withdrawal
clisBNB is automatically burnt or destroyed when users withdraw their BNB or the corresponding slisBNB from Lista DAO’s CDP. This mechanism ensures that clisBNB always accurately represents the amount of BNB in the user’s position. Once the underlying BNB is withdrawn, the associated clisBNB is removed from the system to prevent any mismatches in value. 1:1 Ratio with BNB
When users deposit BNB into a CDP on Lista DAO, they are issued clisBNB at a 1:1 ratio. This direct relationship ensures that for every BNB deposited, one clisBNB is created, maintaining a seamless link between the deposit and the certificate. Non-transferable Token
One important feature of clisBNB is that it is non-transferable. Once issued, clisBNB cannot be moved between wallets or users. It is solely linked to the user’s BNB deposit and exists only within the context of the Lista DAO platform, ensuring its security and focused usage. Automatic burn upon withdrawal
clisBNB is automatically burnt or destroyed when users withdraw their BNB or the corresponding slisBNB from Lista DAO’s CDP. This mechanism ensures that clisBNB always accurately represents the amount of BNB in the user’s position. Once the underlying BNB is withdrawn, the associated clisBNB is removed from the system to prevent any mismatches in value. Minting clisBNB to Another Address Minting clisBNB to Another Address Minting clisBNB to Another Address clisBNB offers users a unique feature: When depositing BNB into a CDP, they have the option to mint clisBNB to another address. However, once this address has been chosen and the clisBNB has been sent over, it will no longer be transferable to any other wallet. Having this function enables greater flexibility, especially for users who may want to delegate certain actions related to another wallet, such as the accumulation of stardust points. However, this flexibility comes with some key restrictions: Only one address can be specified to receive clisBNB at the time of deposit. If the underlying BNB or slisBNB is withdrawn, any clisBNB minted to the secondary address is automatically destroyed. How to Obtain clisBNB Accessing clisBNB is a straightforward process. Users can visit ListaDAO’s deposit page, where they can deposit their BNB into a Collateralized Debt Position (CDP), and automatically receive clisBNB in return. From there, clisBNB can be used to participate in Binance Launchpool events via the Binance Web3 MPC wallet. To ensure participation in Binance Launchpool events, it’s important that users utilize the Binance Web3 MPC wallet, which supports smooth interaction between the staked BNB and launch events. clisBNB: Unlocking BNB Liquidity clisBNB offers BNB holders a powerful new way to unlock liquidity, borrow lisUSD, and participate in Binance Launchpool events without losing access to their collateral. By enhancing the utility and flexibility of staked BNB, clisBNB represents a significant advancement in DeFi solutions for BNB users. With the introduction of clisBNB, BNB holders now have a seamless tool for managing collateral, participating in Binance launchpools and accessing DeFi opportunities, all within a simple and secure process. About BNB XYZ BNB XYZ is a One-Stop on Chain Staking Platform for BNB, designed to bridge the gap between BNB CeFi and DeFi. It offers seamless staking solutions and optimizes the utility of both CeFi BNB and DeFi BNB, making it the go-to resource for all BNB users to maximize their potential earnings. Core contributors of BNB XYZ include PancakeSwap, Lista DAO, and BNB Chain. Website: https://BNB.xyz X (Twitter): https://x.com/BNBxyz Core contributors: PancakeSwap, Lista DAO, BNB chain https://BNB.xyz https://x.com/BNBxyz Contact Marketing team
BNB.xyz
marketing@bnb.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Unlocking New DeFi Opportunities for BNB Holders

فتح فرص DeFi جديدة لحاملي BNB

Desbloqueo de nuevas oportunidades DeFi para los titulares de BNB

BNB धारकों के लिए नए DeFi अवसरों को खोलना

BNB保有者のための新たなDeFi機会の解放

BNB 보유자를 위한 새로운 DeFi 기회 잠금 해제

ການປົດລັອກໂອກາດ DeFi ໃໝ່ສຳລັບຜູ້ຖື BNB

Отклучување на нови можности за DeFi за носителите на BNB

Nieuwe DeFi-kansen ontsluiten voor BNB-houders

Открытие новых возможностей DeFi для держателей BNB

Gufungura amahirwe mashya ya DeFi kubafite BNB

BNB දරන්නන් සඳහා නව DeFi අවස්ථා විවෘත කිරීම

Mở khóa các cơ hội DeFi mới cho người nắm giữ BNB

The rise of decentralized computing, particularly through GPU networks, has generated significant interest in recent years. Lots of emerging projects aim to harness what some see as the untapped potential of distributed computing resources, offering an alternative to traditional cloud-based systems. But a key question remains: is there genuine market demand for decentralized GPU networks? And if so, what are the driving forces behind this demand, and what challenges lie ahead? AI and Machine Learning Driving Demand Artificial intelligence (AI) and machine learning (ML) applications have been at the forefront of demand for decentralized GPU networks. These technologies require substantial computational resources, especially for tasks such as training deep learning models, running large-scale simulations, and processing vast datasets. Traditional GPU solutions from cloud providers like Amazon Web Services (AWS) and Google Cloud can handle these tasks but come with high costs and scalability limitations. In contrast, decentralized GPU networks offer the flexibility to scale while distributing workloads across a global network of computing resources, reducing the risk of failure and boosting efficiency. This decentralized approach can meet the ever-growing computational demands from sectors like healthcare, finance, and autonomous technology. Additionally, with AI applications becoming more widespread, decentralized GPU networks are gaining traction as a more affordable and flexible solution compared to centralized providers. Open-Source Models and Customization The recent developments in open-source models, such as Meta’s LLaMA, have further reinforced the need for decentralized GPU networks. These open-source models provide enterprise-grade performance that can be hosted on decentralized networks, reducing dependency on large centralized providers like OpenAI and Microsoft. The ability to customize and optimize these models to fit specific use cases adds another layer of appeal to decentralized solutions. By adopting decentralized GPU networks, businesses not only cut costs but also achieve greater control over their AI and machine learning deployments, ensuring these systems are tailored precisely to their needs. This adaptability is particularly relevant for industries that rely on high computational power but need the ability to adjust their infrastructure quickly in response to changing demands. Cost Efficiency and Flexibility Cost efficiency remains one of the most compelling benefits of decentralized GPU networks. Traditional cloud providers, while reliable, often become prohibitively expensive as businesses scale. Decentralized networks, by tapping into underutilized GPU resources scattered across the globe, can offer far more competitive pricing. For many businesses, this lower-cost alternative provides access to high-performance computing power without the financial burden associated with centralized cloud services. Moreover, the flexibility inherent in decentralized systems allows businesses to scale their computational needs up or down as required. This elasticity makes decentralized GPU networks highly attractive for organizations with fluctuating demand, offering them the ability to optimize costs while maintaining the necessary performance levels. Challenges and Barriers to Adoption While the benefits of decentralized GPU networks are clear, several challenges must be addressed for widespread adoption to occur. Security and Privacy Concerns Security is a critical issue for any network, but especially so for decentralized systems handling sensitive data. Industries like healthcare, finance, and transportation, which often deal with confidential information, must be assured that decentralized GPU networks can provide robust security measures. Data encryption, privacy protection, and compliance with regulatory standards such as GDPR or HIPAA are essential components that decentralized providers must implement. Without trust in the security of decentralized networks, their adoption will be slow, especially in industries with strict data handling regulations. Scalability and Reliability One of the main promises of decentralized GPU networks is their scalability. However, the infrastructure must demonstrate that it can scale without compromising performance or reliability. Ensuring that decentralized systems can manage resources efficiently, recover from faults quickly, and avoid downtime is key to their success. Consistent uptime and smooth resource allocation will help convince businesses to transition from centralized systems to decentralized networks. Integration and User Experience For decentralized GPU networks to achieve widespread use, the integration process must be seamless. Businesses need solutions that are easy to adopt, with user-friendly interfaces, comprehensive APIs, and support for popular frameworks like TensorFlow or PyTorch. By simplifying the technical barriers to entry, decentralized providers can accelerate the adoption of their solutions among enterprises. The Future of Decentralized GPU Networks The market demand for decentralized GPU networks is indeed growing, fueled primarily by AI, machine learning, and big data applications. These networks offer significant advantages in terms of cost efficiency, scalability, and flexibility. As businesses increasingly look for alternatives to the expensive and often inflexible centralized cloud solutions, decentralized GPU networks present a compelling option. However, to fully realize their potential, the industry must address several critical challenges, including security, scalability, and ease of integration. By overcoming these barriers, decentralized GPU networks can transform the computing landscape, making high-performance computing more accessible, cost-effective, and customizable. In the coming years, decentralized GPU networks could become an essential part of the computing infrastructure for AI, machine learning, and other high-demand sectors. With the right focus on security, reliability, and user experience, these networks have the potential to revolutionize how we approach computational power in a decentralized, efficient, and flexible way. The rise of decentralized computing, particularly through GPU networks, has generated significant interest in recent years. Lots of emerging projects aim to harness what some see as the untapped potential of distributed computing resources, offering an alternative to traditional cloud-based systems. But a key question remains: is there genuine market demand for decentralized GPU networks? And if so, what are the driving forces behind this demand, and what challenges lie ahead? AI and Machine Learning Driving Demand Artificial intelligence (AI) and machine learning (ML) applications have been at the forefront of demand for decentralized GPU networks. These technologies require substantial computational resources, especially for tasks such as training deep learning models, running large-scale simulations, and processing vast datasets. Traditional GPU solutions from cloud providers like Amazon Web Services (AWS) and Google Cloud can handle these tasks but come with high costs and scalability limitations. In contrast, decentralized GPU networks offer the flexibility to scale while distributing workloads across a global network of computing resources, reducing the risk of failure and boosting efficiency. This decentralized approach can meet the ever-growing computational demands from sectors like healthcare, finance, and autonomous technology. Additionally, with AI applications becoming more widespread, decentralized GPU networks are gaining traction as a more affordable and flexible solution compared to centralized providers. Open-Source Models and Customization The recent developments in open-source models, such as Meta’s LLaMA, have further reinforced the need for decentralized GPU networks. These open-source models provide enterprise-grade performance that can be hosted on decentralized networks, reducing dependency on large centralized providers like OpenAI and Microsoft. The ability to customize and optimize these models to fit specific use cases adds another layer of appeal to decentralized solutions. By adopting decentralized GPU networks, businesses not only cut costs but also achieve greater control over their AI and machine learning deployments, ensuring these systems are tailored precisely to their needs. This adaptability is particularly relevant for industries that rely on high computational power but need the ability to adjust their infrastructure quickly in response to changing demands. Cost Efficiency and Flexibility Cost efficiency remains one of the most compelling benefits of decentralized GPU networks. Traditional cloud providers, while reliable, often become prohibitively expensive as businesses scale. Decentralized networks, by tapping into underutilized GPU resources scattered across the globe, can offer far more competitive pricing. For many businesses, this lower-cost alternative provides access to high-performance computing power without the financial burden associated with centralized cloud services. Moreover, the flexibility inherent in decentralized systems allows businesses to scale their computational needs up or down as required. This elasticity makes decentralized GPU networks highly attractive for organizations with fluctuating demand, offering them the ability to optimize costs while maintaining the necessary performance levels. Challenges and Barriers to Adoption While the benefits of decentralized GPU networks are clear, several challenges must be addressed for widespread adoption to occur. Security and Privacy Concerns Security is a critical issue for any network, but especially so for decentralized systems handling sensitive data. Industries like healthcare, finance, and transportation, which often deal with confidential information, must be assured that decentralized GPU networks can provide robust security measures. Data encryption, privacy protection, and compliance with regulatory standards such as GDPR or HIPAA are essential components that decentralized providers must implement. Without trust in the security of decentralized networks, their adoption will be slow, especially in industries with strict data handling regulations. Scalability and Reliability One of the main promises of decentralized GPU networks is their scalability. However, the infrastructure must demonstrate that it can scale without compromising performance or reliability. Ensuring that decentralized systems can manage resources efficiently, recover from faults quickly, and avoid downtime is key to their success. Consistent uptime and smooth resource allocation will help convince businesses to transition from centralized systems to decentralized networks. Integration and User Experience For decentralized GPU networks to achieve widespread use, the integration process must be seamless. Businesses need solutions that are easy to adopt, with user-friendly interfaces, comprehensive APIs, and support for popular frameworks like TensorFlow or PyTorch. By simplifying the technical barriers to entry, decentralized providers can accelerate the adoption of their solutions among enterprises. The Future of Decentralized GPU Networks The market demand for decentralized GPU networks is indeed growing, fueled primarily by AI, machine learning, and big data applications. These networks offer significant advantages in terms of cost efficiency, scalability, and flexibility. As businesses increasingly look for alternatives to the expensive and often inflexible centralized cloud solutions, decentralized GPU networks present a compelling option. However, to fully realize their potential, the industry must address several critical challenges, including security, scalability, and ease of integration. By overcoming these barriers, decentralized GPU networks can transform the computing landscape, making high-performance computing more accessible, cost-effective, and customizable. In the coming years, decentralized GPU networks could become an essential part of the computing infrastructure for AI, machine learning, and other high-demand sectors. With the right focus on security, reliability, and user experience, these networks have the potential to revolutionize how we approach computational power in a decentralized, efficient, and flexible way.

Decentralized GPU Networks: Demand, Challenges, and Future Opportunities

In the face of widespread high attrition, BruntWork has pioneered a proactive strategy that anticipates roles at risk and initiates advanced back-filling. This groundbreaking approach positions BruntWork at the forefront of industry innovation, offering a fresh perspective on how companies can transform attrition challenges into profitable opportunities. Predictive Strategy at the Forefront BruntWork, known for its effective outsourcing solutions, has embraced a predictive strategy to pinpoint roles with a high propensity for attrition. The company's CEO, Winston Ong, explains, "By identifying the areas prone to attrition early, BruntWork developed a proactive back-filling strategy that confronts the issue directly and ensures a seamless operational flow." This predictive model is grounded in thorough data analysis, which helps identify potential high-attrition roles. Once these roles are identified, the strategy is set in motion to back-fill these positions in advance, ensuring that the operational integrity of client companies remains intact. The global outsourcing market is set to soar to $971.2 billion by 2024, driven by a 6.5% CAGR between 2023-2027, underscoring the thriving demand for outsourcing services. The robust growth of the outsourcing industry exemplifies the critical role companies like BruntWork play in the modern business ecosystem. Benefits of Proactive Back-Filling BruntWork's initiative has received widespread acclaim and has demonstrated tangible results. The advanced planning ensures a seamless transition, reducing the operational hiccups typically linked with high attrition roles. As per 2023 data, the proactive back-filling model has effectively shielded businesses from considerable operational downtime. The expedited process of filling vital roles has led to consistent productivity and increased profitability. However, some industry experts express reservations. One expert notes, "While the proactive back-filling strategy is praiseworthy, its applicability across vast enterprises is still uncertain." This perspective suggests that the model's scalability across diverse business sizes warrants further investigation. In a related development, tech startup Betterteem is leveraging AI's predictive prowess to address high attrition rates in the Philippine BPO sector. CEO Bo Discarga states that their technology uses extensive employee data to pinpoint those likely to leave. Notably, tech behemoth IBM had delved into this arena as early as 2019, underscoring AI's growing significance in Human Resources. A Step Towards Redefining Industry Standards BruntWork's successful initiative reflects Winston Ong's visionary leadership. He anticipates a domino effect in the industry: "BruntWork stands at a crossroads where proactive strategies like ours could potentially establish new industry benchmarks." Furthermore, BruntWork's efforts symbolize a broader industry pivot towards inventive, data-centric solutions to tackle challenges. The company's foresight and actionable plans are viewed as a beacon, potentially guiding the industry toward more resilient operational models. Supporting this narrative, 78% of global businesses report positive experiences with their outsourcing partners. This aligns with the broader industry sentiment and suggests a promising trajectory for BruntWork and the sector. BruntWork's bold initiative offers a tangible solution to the high attrition conundrum. It signals a shift towards a more anticipatory, data-informed operational paradigm in outsourcing. As the industry wrestles with attrition, all eyes are on how many will emulate BruntWork's path and the potential transformation of the outsourcing industry's operational framework. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here. In the face of widespread high attrition, BruntWork has pioneered a proactive strategy that anticipates roles at risk and initiates advanced back-filling. This groundbreaking approach positions BruntWork at the forefront of industry innovation, offering a fresh perspective on how companies can transform attrition challenges into profitable opportunities. Predictive Strategy at the Forefront Predictive Strategy at the Forefront BruntWork, known for its effective outsourcing solutions, has embraced a predictive strategy to pinpoint roles with a high propensity for attrition. The company's CEO, Winston Ong, explains, "By identifying the areas prone to attrition early, BruntWork developed a proactive back-filling strategy that confronts the issue directly and ensures a seamless operational flow." "By identifying the areas prone to attrition early, BruntWork developed a proactive back-filling strategy that confronts the issue directly and ensures a seamless operational flow." This predictive model is grounded in thorough data analysis, which helps identify potential high-attrition roles. Once these roles are identified, the strategy is set in motion to back-fill these positions in advance, ensuring that the operational integrity of client companies remains intact. The global outsourcing market is set to soar to $971.2 billion by 2024, driven by a 6.5% CAGR between 2023-2027, underscoring the thriving demand for outsourcing services . The robust growth of the outsourcing industry exemplifies the critical role companies like BruntWork play in the modern business ecosystem. outsourcing services outsourcing services Benefits of Proactive Back-Filling Benefits of Proactive Back-Filling BruntWork's initiative has received widespread acclaim and has demonstrated tangible results. The advanced planning ensures a seamless transition, reducing the operational hiccups typically linked with high attrition roles. As per 2023 data, the proactive back-filling model has effectively shielded businesses from considerable operational downtime. The expedited process of filling vital roles has led to consistent productivity and increased profitability. However, some industry experts express reservations. One expert notes, "While the proactive back-filling strategy is praiseworthy, its applicability across vast enterprises is still uncertain." This perspective suggests that the model's scalability across diverse business sizes warrants further investigation. "While the proactive back-filling strategy is praiseworthy, its applicability across vast enterprises is still uncertain." In a related development, tech startup Betterteem is leveraging AI's predictive prowess to address high attrition rates in the Philippine BPO sector. CEO Bo Discarga states that their technology uses extensive employee data to pinpoint those likely to leave. Notably, tech behemoth IBM had delved into this arena as early as 2019, underscoring AI's growing significance in Human Resources. A Step Towards Redefining Industry Standards A Step Towards Redefining Industry Standards BruntWork's successful initiative reflects Winston Ong's visionary leadership. He anticipates a domino effect in the industry: " BruntWork stands at a crossroads where proactive strategies like ours could potentially establish new industry benchmarks." BruntWork stands at a crossroads where proactive strategies like ours could potentially establish new industry benchmarks." Furthermore, BruntWork's efforts symbolize a broader industry pivot towards inventive, data-centric solutions to tackle challenges. The company's foresight and actionable plans are viewed as a beacon, potentially guiding the industry toward more resilient operational models. Supporting this narrative, 78% of global businesses report positive experiences with their outsourcing partners. This aligns with the broader industry sentiment and suggests a promising trajectory for BruntWork and the sector. BruntWork's bold initiative offers a tangible solution to the high attrition conundrum. It signals a shift towards a more anticipatory, data-informed operational paradigm in outsourcing. As the industry wrestles with attrition, all eyes are on how many will emulate BruntWork's path and the potential transformation of the outsourcing industry's operational framework. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here . here

BruntWork's Proactive Strategy Transforms High Attrition Roles Into Opportunities: Here's How

BruntWork এর সক্রিয় কৌশল উচ্চ অ্যাট্রিশন ভূমিকাকে সুযোগে রূপান্তরিত করে: এখানে কিভাবে

Die proaktive Strategie von BruntWork verwandelt Stellen mit hoher Fluktuation in Chancen: So geht's

La estrategia proactiva de BruntWork transforma los roles de alto desgaste en oportunidades: así es como

La stratégie proactive de BruntWork transforme les postes à fort taux d'attrition en opportunités : voici comment

ब्रंटवर्क की सक्रिय रणनीति उच्च त्यागाव वाली भूमिकाओं को अवसरों में बदल देती है: यहां बताया गया है कि कैसे

BruntWork の積極的な戦略により、離職率の高い役割が機会に変わります: その方法は次のとおりです

BruntWork의 사전 예방적 전략을 통해 이직률이 높은 역할을 기회로 전환: 방법은 다음과 같습니다.

A estratégia proativa da BruntWork transforma funções de alto desgaste em oportunidades: veja como

Проактивная стратегия BruntWork превращает должности с высоким оттоком в возможности: вот как

BruntWork'ün Proaktif Stratejisi Yüksek Yıpratma Rollerini Fırsatlara Dönüştürüyor: İşte Nasıl Yapılacağı

Chiến lược chủ động của BruntWork biến những vai trò tiêu hao nhiều thành cơ hội: Đây là cách

BruntWork 的主动战略将高流失率的角色转变为机会：具体方法如下

Black Friday is nearly here, which means customers will be swarming stores to snag the best deals — and retailers will be bracing for the corresponding boost in thefts. But even aside from the Black Friday bump, theft is a growing problem for many retailers. How could AI reduce the risk? 1. Enabling Automatic Cart Audits Some retailers have employees standing near store exits to check shoppers’ receipts as they leave, verifying that the list of items matches their cart contents. However, this process often becomes cumbersome during busy periods, requiring people to wait in lines. Leaders at Sam’s Club believe AI can do the task faster. As of April 2024, more than 120 locations used AI exit scanning technology, and the overall figure matched executives’ goal of adding AI to 20% of its stores by the end of the first quarter. Sam’s Club represents the largest deployment of such customer-facing technology in the retail industry. Customers who engage with it can leave stores 23% faster, and internal data shows more than 50% of shoppers use it. Notably, before rolling out this solution, Sam’s Club employees got feedback from customers about current pain points and what they wanted from their experiences. Participants mentioned they’d like faster and more convenient shopping trips and consistently brought up the waiting times associated with the previous cart audits as a primary downside.  Now, computer-vision cameras and digital technologies work in the background to check that cart contents match a customer’s payment records. This high-tech approach aligns with the company’s other improvements, such as its Scan & Go offering, which allows people to bypass the traditional checkout process when buying their items. The market value for AI products in retail was approximately $5 billion in 2023, and it’s expected to grow. Decision-makers are even more likely to reap the benefits if they do as Sam’s Club leaders did and gather customer feedback. 2. Curbing Potentially Harmful Employee Preferentiality Most people think of customers as the main people behind retail theft, but workers can exacerbate the problem, too. Sometimes, they might slip small items into their pockets during slow periods, believing no one will notice and they will never get caught. The circumstances can also become more complicated. One example is what some in the retail industry call “sweethearting.” It happens when employees give discounts or free items to friends or relatives. One tech company, Corsight AI, began offering AI technology to detect such interactions. More specifically, it spots unusual behavior by analyzing how close customers stand to employees. Because the tool tracks engagements over time, it can also determine if specific shoppers keep going to the same employee’s cash register or otherwise show a preference. The system flags unusual behaviors to the relevant store decision-makers, letting them decide whether to act. Critics have pointed out that regularly engaging with the same employees when shopping at a store doesn’t necessarily mean anything suspicious. Perhaps a customer appreciates a worker’s friendliness, efficiency or product knowledge, causing them to naturally interact when opportunities arise. However, increasing theft problems have caused some retail leaders to scrutinize things they might have previously ignored. That is particularly true for retailers selling high-value items. For example, jewelry companies’ crime-related losses exceeded $129 million in 2022. These retailers usually have high-tech security systems, but those defenses probably won’t catch occasional instances of store workers offering unusually generous discounts. AI tools might. 3. Providing Better Awareness Although circumstances vary, some potential shoplifters target locations before attempting to steal things. During each visit, they gather information such as the number of workers in the store at particular times, whether certain departments or products have dedicated security measures and the locations of entrances and exits. Someone who goes to a well-staffed store, notices numerous security cameras and sees how employees seem continually aware of their surroundings may decide against proceeding with theft attempt plans. Conversely, stores that seem virtually deserted and feature few or no visible security cameras are much more appealing to would-be thieves. Because AI products can work in the background and detect unusual occurrences, they help loss prevention officers and others become more aware of what happens in individual stores. For example, Everseen creates AI tech that can be used to help reduce self-checkout theft. Part of its approach involves processing footage from ceiling-mounted cameras, often gathering tens of millions of customer interactions daily. Another helpful aspect of the technology is that it can scan shelves to determine when specific products are almost out of stock. Additionally, retail leaders can use this product to get near-real-time analytics about what’s happening in a store and receive prompts about handling certain situations. For example, if the AI spots a frustrated customer, it might prompt a worker to engage with them to learn what’s wrong. Many retailers already use artificial intelligence creatively by incorporating it into their demand-planning processes. For example, automating supply chain activities could reduce costs by as much as 10%, making that goal a worthy pursuit for cost-conscious parties. In any case, improved awareness provided by specialty tech products could help retailers manage theft. 4. Analyzing Shoppers’ Gestures Although many retailers use facial recognition technologies, some consumers dislike the perceived lack of privacy. An AI alternative is potentially less invasive because it does not analyze people’s appearance, but rather whether they make suspicious movements commonly linked to theft. Wild Duck Wine & Spirits in Boston uses an AI product from company Veesion on all 24 of its security camera feeds. The technology checks shoppers’ gestures to see if they match those frequently used during shoplifting. For example, someone might try to conceal something in a clothing pocket. Another “red flag” occurs when people hide items in baby strollers. Finally, people may eat or drink while shopping, potentially consuming what they have not paid for yet. If the AI spots something specific, it alerts workers approximately 15 seconds later, providing a video clip of the event. Those employees can then choose whether to confront the person or see if they try to skip the checkout counter. A representative from the AI company explained that this solution is about more than stopping people from stealing single items. It sends them a message that they should not try to repeat their actions because they’ll get caught. Minimizing Theft in Retail These fascinating examples show how AI can help store managers, loss prevention officers and other responsible parties reduce the frequency of theft in their locations. Choosing the appropriate tools and learning how to use them properly is crucial for success. Black Friday is nearly here, which means customers will be swarming stores to snag the best deals — and retailers will be bracing for the corresponding boost in thefts. But even aside from the Black Friday bump, theft is a growing problem for many retailers. How could AI reduce the risk? Black Friday is nearly here, which means customers will be swarming stores to snag the best deals — and retailers will be bracing for the corresponding boost in thefts. But even aside from the Black Friday bump, theft is a growing problem for many retailers. How could AI reduce the risk? 1. Enabling Automatic Cart Audits 1. Enabling Automatic Cart Audits Some retailers have employees standing near store exits to check shoppers’ receipts as they leave, verifying that the list of items matches their cart contents. However, this process often becomes cumbersome during busy periods, requiring people to wait in lines. Leaders at Sam’s Club believe AI can do the task faster. As of April 2024, more than 120 locations used AI exit scanning technology, and the overall figure matched executives’ goal of adding AI to 20% of its stores by the end of the first quarter. Sam’s Club represents the largest deployment of such customer-facing technology in the retail industry. Customers who engage with it can leave stores 23% faster , and internal data shows more than 50% of shoppers use it. Notably, before rolling out this solution, Sam’s Club employees got feedback from customers about current pain points and what they wanted from their experiences. leave stores 23% faster leave stores 23% faster Participants mentioned they’d like faster and more convenient shopping trips and consistently brought up the waiting times associated with the previous cart audits as a primary downside.  Now, computer-vision cameras and digital technologies work in the background to check that cart contents match a customer’s payment records. This high-tech approach aligns with the company’s other improvements, such as its Scan & Go offering, which allows people to bypass the traditional checkout process when buying their items. The market value for AI products in retail was approximately $5 billion in 2023 , and it’s expected to grow. Decision-makers are even more likely to reap the benefits if they do as Sam’s Club leaders did and gather customer feedback. approximately $5 billion in 2023 approximately $5 billion in 2023 2. Curbing Potentially Harmful Employee Preferentiality Most people think of customers as the main people behind retail theft, but workers can exacerbate the problem, too. Sometimes, they might slip small items into their pockets during slow periods, believing no one will notice and they will never get caught. The circumstances can also become more complicated. One example is what some in the retail industry call “sweethearting.” It happens when employees give discounts or free items to friends or relatives. One tech company, Corsight AI, began offering AI technology to detect such interactions. More specifically, it spots unusual behavior by analyzing how close customers stand to employees. Because the tool tracks engagements over time, it can also determine if specific shoppers keep going to the same employee’s cash register or otherwise show a preference. The system flags unusual behaviors to the relevant store decision-makers, letting them decide whether to act. spots unusual behavior spots unusual behavior Critics have pointed out that regularly engaging with the same employees when shopping at a store doesn’t necessarily mean anything suspicious. Perhaps a customer appreciates a worker’s friendliness, efficiency or product knowledge, causing them to naturally interact when opportunities arise. However, increasing theft problems have caused some retail leaders to scrutinize things they might have previously ignored. That is particularly true for retailers selling high-value items. For example, jewelry companies’ crime-related losses exceeded $129 million in 2022. These retailers usually have high-tech security systems, but those defenses probably won’t catch occasional instances of store workers offering unusually generous discounts. AI tools might. 3. Providing Better Awareness Although circumstances vary, some potential shoplifters target locations before attempting to steal things. During each visit, they gather information such as the number of workers in the store at particular times, whether certain departments or products have dedicated security measures and the locations of entrances and exits. Someone who goes to a well-staffed store, notices numerous security cameras and sees how employees seem continually aware of their surroundings may decide against proceeding with theft attempt plans. Conversely, stores that seem virtually deserted and feature few or no visible security cameras are much more appealing to would-be thieves. Because AI products can work in the background and detect unusual occurrences, they help loss prevention officers and others become more aware of what happens in individual stores. For example, Everseen creates AI tech that can be used to help reduce self-checkout theft. Part of its approach involves processing footage from ceiling-mounted cameras, often gathering tens of millions of customer interactions daily. Another helpful aspect of the technology is that it can scan shelves to determine when specific products are almost out of stock. Additionally, retail leaders can use this product to get near-real-time analytics about what’s happening in a store and receive prompts about handling certain situations. For example, if the AI spots a frustrated customer, it might prompt a worker to engage with them to learn what’s wrong. Many retailers already use artificial intelligence creatively by incorporating it into their demand-planning processes. For example, automating supply chain activities could reduce costs by as much as 10% , making that goal a worthy pursuit for cost-conscious parties. In any case, improved awareness provided by specialty tech products could help retailers manage theft. by as much as 10% by as much as 10% 4. Analyzing Shoppers’ Gestures Although many retailers use facial recognition technologies, some consumers dislike the perceived lack of privacy. An AI alternative is potentially less invasive because it does not analyze people’s appearance, but rather whether they make suspicious movements commonly linked to theft. Wild Duck Wine & Spirits in Boston uses an AI product from company Veesion on all 24 of its security camera feeds. The technology checks shoppers’ gestures to see if they match those frequently used during shoplifting. For example, someone might try to conceal something in a clothing pocket. Another “red flag” occurs when people hide items in baby strollers. Finally, people may eat or drink while shopping, potentially consuming what they have not paid for yet. If the AI spots something specific, it alerts workers approximately 15 seconds later , providing a video clip of the event. Those employees can then choose whether to confront the person or see if they try to skip the checkout counter. approximately 15 seconds later approximately 15 seconds later A representative from the AI company explained that this solution is about more than stopping people from stealing single items. It sends them a message that they should not try to repeat their actions because they’ll get caught. Minimizing Theft in Retail These fascinating examples show how AI can help store managers, loss prevention officers and other responsible parties reduce the frequency of theft in their locations. Choosing the appropriate tools and learning how to use them properly is crucial for success.

With Black Friday Coming Up, Retailers Are Better Off Using AI to Combat Theft

Gradient Descent. Image taken from - https://community.deeplearning.ai/t/difference-between-rmsprop-and-adam/310187 What Is Gradient Descent? Gradient descent is like hiking downhill with your eyes closed, following the slope until you hit the bottom (or at least a nice flat spot to rest). Technically, it is a method to minimize an objective function F(θ), parameterized by a model’s parameters θ∈Rn, by updating them in the opposite direction of the gradient ∇F(θ). The size of each step is controlled by the learning rate α. Think of α as the cautious guide who ensures you don’t tumble down too fast. If this sounds like Greek, feel free to check out my previous article, Quick Glance At Gradient Descent In Machine Learning — I promise it’s friendlier than it sounds! 🙂 Circle back: An objective function is the mathematical formula or function that your model aims to minimize (or maximize, depending on the goal) during training. It represents a measure of how far off your model’s predictions are from the actual outcomes or desired values.
For example:In machine learning, it could be a loss function like Mean Squared Error (MSE) for regression tasks or Cross-Entropy Loss for classification tasks.The objective function maps the model’s predictions to a numerical value, where smaller values indicate better performance.In simpler terms:It’s like a “fitness tracker” for your model — it tells you how good or bad your model’s predictions are. During optimization, gradient descent helps adjust the model’s parameters θ to reduce this value step by step, moving closer to an ideal solution. Got it? 😄 Gradient Descent Variants Gradient descent isn’t a one-size-fits-all deal. It comes in three variants, each like a different hiker — some take the scenic route, others sprint downhill, and a few prefer shortcuts (like me 😅). These variants balance accuracy and speed, depending on how much data they use to calculate the gradient. 1. Batch Gradient Descent Batch gradient descent, the “all-or-nothing” hiker, uses the entire dataset to compute the gradient of the cost function: Imagine stopping to look 👀 at every rock 🪨, tree 🌴, and bird 🕊 before deciding where to place your foot 🦶🏽next. It’s thorough, but not ideal if your dataset is as big as, say, the Amazon rainforest 😩. It’s also not great if you need to learn on the fly — like updating your hiking route after spotting a bear 🐻‍❄️. Code Example: for i in range(nb_epochs):
    params_grad = evaluate_gradient(loss_function, data, params)
    params = params - learning_rate * params_grad Batch gradient descent shines when you have all the time in the world and a dataset that fits neatly into memory. It’s guaranteed to find the global minimum for convex surfaces (smooth hills) or a local minimum for non-convex surfaces (rugged mountains). 2. Stochastic Gradient Descent (SGD) SGD is the “impulsive” hiker who takes one step at a time based on the current terrain: It’s faster because it doesn’t bother calculating gradients for the entire landscape. Instead, it uses one training example at a time. While this saves time, the frequent updates can make SGD look like it’s zigzagging downhill, which can be both exciting and a little chaotic. 😅 Imagine updating your grocery list after each aisle — you get to the essentials faster, but your cart might look wild in the process. However, with a learning rate that slows down over time, SGD can eventually reach the bottom (or the best local minimum). Code Example: for i in range(nb_epochs):
    np.random.shuffle(data)
    for example in data:
        params_grad = evaluate_gradient(loss_function, example, params)
        params = params - learning_rate * params_grad 3. The Hero of the Day: Adam 🙌🏽 Now let’s talk about Adam — the “hiking guru” who combines the wisdom of Momentum and RMSprop. Adaptive Moment Estimation (Adam) is like having a smart guide who tracks the terrain and adjusts your steps based on past experiences and current conditions. It’s the go-to optimizer when you want to train neural networks and still have time for coffee. Why You’ll Love Adam Low Memory Requirements: It’s like carrying a lightweight backpack — efficient but still packed with essentials.


Minimal Hyperparameter Tuning: Adam works well out of the box, so you won’t need to fiddle with too many knobs (just keep an eye on the learning rate).


Practical Use: From improving product recommendations to recognizing images of cats on the internet, Adam powers machine learning systems that make your everyday tech smarter. How Adam Works Adam maintains moving averages of gradients (mt​) and squared gradients (vt​) to adapt learning rates for each parameter: Here’s the cool part: Adam corrects biases to ensure accuracy, and updates parameters using this formula: where m^t and v^t are bias-corrected estimates, and epsilon ϵ is a small number to prevent division by zero. Conclusion Adam is the Swiss Army knife of optimizers — versatile, efficient, and reliable. Whether you’re training neural networks to detect fraud or create next-gen chatbots, Adam helps you get there faster and with fewer headaches. So, embrace Adam, take confident steps, and enjoy the view from the summit of machine learning success! References: https://www.ceremade.dauphine.fr/\~waldspurger/tds/22_23_s1/advanced_gradient_descent.pdf https://www.geeksforgeeks.org/rmsprop-optimizer-in-deep-learning/ Gradient Descent. Image taken from - https://community.deeplearning.ai/t/difference-between-rmsprop-and-adam/310187 Gradient Descent. Image taken from - https://community.deeplearning.ai/t/difference-between-rmsprop-and-adam/310187 What Is Gradient Descent? Gradient descent is like hiking downhill with your eyes closed, following the slope until you hit the bottom (or at least a nice flat spot to rest). Technically, it is a method to minimize an objective function F(θ ), parameterized by a model’s parameters θ∈Rn, by updating them in the opposite direction of the gradient ∇F(θ). objective function F(θ The size of each step is controlled by the learning rate α. Think of α as the cautious guide who ensures you don’t tumble down too fast. If this sounds like Greek, feel free to check out my previous article, Quick Glance At Gradient Descent In Machine Learning — I promise it’s friendlier than it sounds! 🙂 Circle back: An objective function is the mathematical formula or function that your model aims to minimize (or maximize, depending on the goal) during training. It represents a measure of how far off your model’s predictions are from the actual outcomes or desired values. For example: In machine learning, it could be a loss function like Mean Squared Error (MSE) for regression tasks or Cross-Entropy Loss for classification tasks. The objective function maps the model’s predictions to a numerical value, where smaller values indicate better performance. In simpler terms: It’s like a “fitness tracker” for your model — it tells you how good or bad your model’s predictions are. During optimization, gradient descent helps adjust the model’s parameters θ to reduce this value step by step, moving closer to an ideal solution. Got it? 😄 Circle back: An objective function is the mathematical formula or function that your model aims to minimize (or maximize, depending on the goal) during training. It represents a measure of how far off your model’s predictions are from the actual outcomes or desired values. Circle back: objective function For example: In machine learning , it could be a loss function like Mean Squared Error (MSE) for regression tasks or Cross-Entropy Loss for classification tasks. machine learning loss function The objective function maps the model’s predictions to a numerical value , where smaller values indicate better performance. numerical value In simpler terms: It’s like a “fitness tracker” for your model — it tells you how good or bad your model’s predictions are. During optimization, gradient descent helps adjust the model’s parameters θ to reduce this value step by step, moving closer to an ideal solution. Got it? 😄 Gradient Descent Variants Gradient descent isn’t a one-size-fits-all deal. It comes in three variants, each like a different hiker — some take the scenic route, others sprint downhill, and a few prefer shortcuts (like me 😅). These variants balance accuracy and speed, depending on how much data they use to calculate the gradient. 1. Batch Gradient Descent Batch gradient descent, the “all-or-nothing” hiker, uses the entire dataset to compute the gradient of the cost function: Imagine stopping to look 👀 at every rock 🪨, tree 🌴, and bird 🕊 before deciding where to place your foot 🦶🏽next. It’s thorough, but not ideal if your dataset is as big as, say, the Amazon rainforest 😩. It’s also not great if you need to learn on the fly — like updating your hiking route after spotting a bear 🐻‍❄️. Code Example: Code Example: for i in range(nb_epochs):
    params_grad = evaluate_gradient(loss_function, data, params)
    params = params - learning_rate * params_grad for i in range(nb_epochs):
    params_grad = evaluate_gradient(loss_function, data, params)
    params = params - learning_rate * params_grad Batch gradient descent shines when you have all the time in the world and a dataset that fits neatly into memory. It’s guaranteed to find the global minimum for convex surfaces (smooth hills) or a local minimum for non-convex surfaces (rugged mountains). 2. Stochastic Gradient Descent (SGD) SGD is the “impulsive” hiker who takes one step at a time based on the current terrain: It’s faster because it doesn’t bother calculating gradients for the entire landscape. Instead, it uses one training example at a time. While this saves time, the frequent updates can make SGD look like it’s zigzagging downhill, which can be both exciting and a little chaotic. 😅 Imagine updating your grocery list after each aisle — you get to the essentials faster, but your cart might look wild in the process. However, with a learning rate that slows down over time, SGD can eventually reach the bottom (or the best local minimum). Code Example: Code Example: for i in range(nb_epochs):
    np.random.shuffle(data)
    for example in data:
        params_grad = evaluate_gradient(loss_function, example, params)
        params = params - learning_rate * params_grad for i in range(nb_epochs):
    np.random.shuffle(data)
    for example in data:
        params_grad = evaluate_gradient(loss_function, example, params)
        params = params - learning_rate * params_grad 3. The Hero of the Day: Adam 🙌🏽 Now let’s talk about Adam — the “hiking guru” who combines the wisdom of Momentum and RMSprop . Adaptive Moment Estimation (Adam) is like having a smart guide who tracks the terrain and adjusts your steps based on past experiences and current conditions. It’s the go-to optimizer when you want to train neural networks and still have time for coffee. Momentum RMSprop Why You’ll Love Adam Low Memory Requirements: It’s like carrying a lightweight backpack — efficient but still packed with essentials. Minimal Hyperparameter Tuning: Adam works well out of the box, so you won’t need to fiddle with too many knobs (just keep an eye on the learning rate). Practical Use: From improving product recommendations to recognizing images of cats on the internet, Adam powers machine learning systems that make your everyday tech smarter. Low Memory Requirements: It’s like carrying a lightweight backpack — efficient but still packed with essentials. Low Memory Requirements: It’s like carrying a lightweight backpack — efficient but still packed with essentials. Low Memory Requirements: Minimal Hyperparameter Tuning: Adam works well out of the box, so you won’t need to fiddle with too many knobs (just keep an eye on the learning rate). Minimal Hyperparameter Tuning: Adam works well out of the box, so you won’t need to fiddle with too many knobs (just keep an eye on the learning rate). Minimal Hyperparameter Tuning: Practical Use: From improving product recommendations to recognizing images of cats on the internet, Adam powers machine learning systems that make your everyday tech smarter. Practical Use: From improving product recommendations to recognizing images of cats on the internet, Adam powers machine learning systems that make your everyday tech smarter. Practical Use: How Adam Works Adam maintains moving averages of gradients (mt​) and squared gradients (vt​) to adapt learning rates for each parameter: Here’s the cool part: Adam corrects biases to ensure accuracy, and updates parameters using this formula: where m^t and v^t are bias-corrected estimates, and epsilon ϵ is a small number to prevent division by zero. Conclusion Adam is the Swiss Army knife of optimizers — versatile, efficient, and reliable. Whether you’re training neural networks to detect fraud or create next-gen chatbots, Adam helps you get there faster and with fewer headaches. So, embrace Adam, take confident steps, and enjoy the view from the summit of machine learning success! References: https://www.ceremade.dauphine.fr/\~waldspurger/tds/22_23_s1/advanced_gradient_descent.pdf https://www.ceremade.dauphine.fr/\~waldspurger/tds/22_23_s1/advanced_gradient_descent.pdf https://www.geeksforgeeks.org/rmsprop-optimizer-in-deep-learning/ https://www.geeksforgeeks.org/rmsprop-optimizer-in-deep-learning/

Extending Stochastic Gradient Optimization with ADAM

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