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Go Clean to Be Lean: Data Optimization for Improved Business Efficiency

Source: By Author ; possibilities of AI are innumerable and they easily surpass our most artistically fecund imaginations. We are really close to living in some sort of sci-fi, so it’s a good idea to have a look at the most possible and promising machine learning and and ask ourselves if we are ready for them. What all we read in science fiction novels or saw in movies like ‘The Matrix’ could someday materialize into reality. Artificial Intelligence AI trends for the upcoming 2019 In science fiction, , or they are benevolent caretakers of our species. In reality, machine-learning is already with us, evolving out of search engines like Google and seeping into our everyday lives without much fanfare. artificial intelligence (AI) systems are often bent on overthrowing human civilization Artificial intelligence is 360 solution for business and government leaders review the right moves. But what’s happens in the lab, where discoveries by for the coming year and beyond? Team of researchers from Artificial Intelligence Lab homed in on the leading developments both technologists and business leaders should watch closely. academic and corporate researchers will set AI’s course Courtesy: Google.com Bill Gates, the founder of Microsoft, once said that ‘AI can be our friend’ and is good for the society. Let’s have a look at the that will have a huge impact in years to come. trends In AI in 2018 ORIGINALLY POSTED: https://www.pwchk.com/en/industries/telecommunications-media-and-technology/publications/top-10-ai-technology-trends-for-2018.html Deep learning theory: The information bottleneck principle explains how a deep neural network learns. Deep neural networks, which mimic the human brain, have demonstrated their ability to “learn” from image, audio, and text data. Yet even after being in use for more than a decade, there’s still a lot we don’t yet know about deep learning, including how neural networks learn or why they perform so well. That may be changing, thanks to a new theory that applies the principle of an information bottleneck to deep learning. In essence, it suggests that after an initial fitting phase, a deep neural network will “forget” and compress noisy data — that is, data sets containing a lot of additional meaningless information — while still preserving information about what the data represents. What it is: Understanding precisely how deep learning works enables its greater development and use. For example, it can yield insights into optimal network design and architecture choices, while providing increased transparency for safety-critical or regulatory applications. Expect to see more results from the exploration of this theory applied to other types of deep neural networks and deep neural network design. Why it matters: Capsule Networks: New type of deep neural network that learns with fewer errors and less data, by preserving key hierarchical relationships. Capsule networks, a new type of deep neural network, process visual information in much the same way as the brain, which means they can maintain hierarchical relationships. This is in stark contrast to convolutional neural networks, one of the most widely used neural networks, which fail to take into account important spatial hierarchies between simple and complex objects, resulting in miss-classification and a high error rate. What it is: For typical identification tasks, capsule networks promise better accuracy via reduction of errors- by as much as 50 percent. They also don’t need as much data for training models. Expect to see the widespread use of capsule networks across many problem domains and deep neural network architectures. Why it matters: Deep Reinforcement Learning: This technique combines reinforcement learning with deep neural networks to learn by interacting with the environment. A type of neural network that learns by interacting with the environment through observations, actions, and rewards. Deep reinforcement learning (DRL) has been used to learn gaming strategies, such as Atari and Go — including the famous AlphaGo program that beat a human champion. What it is: (DRL)Deep reinforcement learning is the most general purpose of all learning techniques, so it can be used in most business applications. It requires less data than other techniques to train its models. Even more notable is that it can be trained via simulation, which eliminates the need for labeled data. Given these advantages, expect to see more business applications that combine DRL and agent-based simulation in the coming year. Why it matters: Generative Adversarial Networks: A type of unsupervised deep learning system, implemented as two competing neural networks, enabling machine learning with less human intervention. A generative adversarial network (GAN) is a type of unsupervised deep learning system that is implemented as two competing neural networks. One network, the generator, creates fake data that looks exactly like the real data set. The second network, the discriminator, ingests real and synthetic data. Over time, each network improves, enabling the pair to learn the entire distribution of the given data set. What it is: GANs open up deep learning to a larger range of unsupervised tasks in which labeled data does not exist or is too expensive to obtain. They also reduce the load required for a deep neural network because the two networks share the burden. Expect to see more business applications, such as cyber detection, employ GANs. Why it matters: Lean & Augmented Data Learning: Different techniques that enable a model to learn from less data or synthetic data. The biggest challenge in machine learning (deep learning, in particular), is the availability of large volumes of labeled data to train the system. Two broad techniques can help address this: (1) synthesizing new data and (2) transferring a model trained for one task or domain to another. Techniques, such as transfer learning (transferring the insights learned from one task/domain to another) or one-shot learning (transfer learning taken to the extreme with learning occurring with just one or no relevant examples) — making them “lean data” learning techniques. Similarly, synthesizing new data through simulations or interpolations helps obtain more data, thereby augmenting existing data to improve learning. What it is: Using these techniques, we can address a wider variety of problems, especially those with less historical data. Expect to see more variations of lean and augmented data, as well as different types of learning applied to a broad range of business problems. Why it matters: Probabilistic Programming: A high-level language that makes it easy for developers to define probability models. A high-level programming language that more easily enables a developer to design probability models and then automatically “solve” these models. languages make it possible to reuse model libraries, support interactive modeling and formal verification, and provide the abstraction layer necessary to foster generic, efficient inference in universal model classes. What it is: Probabilistic programming Probabilistic programming languages have the ability to accommodate the uncertain and incomplete information that is so common in the business domain. We will see wider adoption of these languages and expect them to also be applied to deep learning. Why it matters: Hybrid Learning Models: Approach that combines different types of deep neural networks with probabilistic approaches to model uncertainty. Different types of deep neural networks, such as GANs or DRL, have shown great promise in terms of their performance and widespread application with different types of data. However, deep learning models do not , the way Bayesian, or probabilistic, approaches do. Hybrid learning models combine the two approaches to leverage the strengths of each. Some examples of hybrid models are Bayesian deep learning, Bayesian GANs, and . What it is: model uncertainty Bayesian conditional GANs Hybrid learning models make it possible to expand the variety of business problems to include deep learning with uncertainty. This can help us achieve better performance and explainability of models, which in turn could encourage more widespread adoption. Expect to see more deep learning methods gain Bayesian equivalents while a combination of probabilistic programming languages start to incorporate deep learning. Why it matters: Automated Machine Learning: Technique for automating the standard workflow of machine learning. Developing machine learning models requires a time-consuming and expert-driven workflow, which includes data preparation, feature selection, model or technique selection, training, and tuning. AutoML aims to automate this workflow using a number of different statistical and deep learning techniques. What it is: AutoML is part of what’s seen as a democratization of AI tools, enabling business users to develop machine learning models without a deep programming background. It will also speed up the time it takes data scientists to create models. Expect to see more commercial AutoML packages and integration of AutoML within larger machine learning platforms. Why it matters: Digital Twin: A virtual model used to facilitate detailed analysis and monitoring of physical or psychological systems. A digital twin is a virtual model used to facilitate detailed analysis and monitoring of physical or psychological systems. The concept of the digital twin where it has been used widely to analyze and monitor things like windmill farms or industrial systems. Now, using agent-based modeling (computational models for simulating the actions and interactions of autonomous agents) and system dynamics (a computer-aided approach to policy analysis and design), digital twins are being applied to nonphysical objects and processes, including predicting customer behavior. What it is: originated in the industrial world Digital twins can help spur the development and broader adopting of the internet of things (IoT), providing a way to predictable diagnosis and maintain IoT systems. Going forward, expect to see greater use of digital twins in both physical systems and consumer choice modeling. Why it matters: Explainable Artificial Intelligence: Machine learning techniques that produce more explainable models while maintaining high performance. Today, there are scores of machine learning algorithms in use that sense, think, and act in a variety of different applications. Yet many of these algorithms are considered “black boxes,” offering little if any insight into how they reached their outcome. Explainable AI is a movement to develop machine learning techniques that produce more explainable models while maintaining prediction accuracy. What it is: AI that is explainable, provable, and transparent will be critical to establishing trust in the technology and will encourage wider adoption of machine learning techniques. Enterprises will adopt explainable AI as a requirement or best practice before embarking on widespread deployment of AI, while governments may make explainable AI a regulatory requirement in the future. Why it matters: Courtesy: Google.com Conclusion Overall, artificial intelligence is a fantastic opportunity for scientists and developers all over the world to make a sustainable ecosystem that’s more efficient for multiple purposes. The chance of machine making a mistake is much less than a human making a mistake; the state of development in multiple fields will shift to the whole new level. Like , eCommerce, logistics industries & autonomous factories are the few examples who are logically and globally accepting and implementing the trend of AI and IoT. If you are looking for any sort of consultants & development resources of machine learning & AI, you may get the assistance. healthcare

Top Artificial Intelligence Trends You Should Consider For AI & Machine Learning Development

Originally published at www.Tintup.com/casestudy — April 13, 2016 #getunstuck is sales video micro-mentorship at scale and it launches today. The largest campaign of its kind: by thought leaders in sales & business, for the new generation of the aspiring best. The movement empowers millennial salespeople to #getunstuck and share their own story. The Challenge of salespeople today: One traditional perception of salespeople is that they are solely money-motivated and just doing whatever it takes to close deals. Sales as a profession is shifting — the reality in the current business landscape is that salespeople are constantly seeking fulfillment to better themselves by honing their craft. Despite popular perception, sales is a very complex discipline and can be a very solitary lifestyle filled with obstacles. Perception vs. reality: It’s challenging to find quality content amongst the vast amount out there. Salespeople want the feeling of having a mentor, someone who shares gold nuggets of wisdom with them, but as they do the sales grind day in and day out, it’s not easy to find that valuable insight. We are providing this mentorship at scale by bringing together an unprecedented number of the brightest minds and gathering all their distilled wisdom in one place. Mentorship at scale: internet Salespeople aren’t seeking out long form articles to better themselves. The way we consume content has changed — we chose the 60-second format to truly suit the millennial audience. How do millennials consume content? The Solution: To raise #getunstuck awareness and engagement with TINT’s web display solution. To initiate the campaign, we reached out to the top sales thought leaders and have already begun receiving community submissions on . www.getunstuck.club The companies participating include SalesLoft, Marin Software, CenturyLink, Influitive, SAP, Fastly, Tint, Hubspot, Salesforce, UC Berkeley Extension, LeanData, HackerRank, HireVue, Dyn, TalentBin, Reward Gateway, Birst and more. As well as our favorite thought leaders from Predictable Revenue, A Sales Guy, Score More Sales, Sales Pro Insider, Heinz , Sales Trainer, Sales Hacker, Harris Consulting, Creation Agency, Sales-Shake and more. Marketing To amplify engagement, we’ll be facilitating a #getunstuck daily tweet discussion from @getunstuckdaily around the featured videos of the day. “TINT has enabled us to unite top sales leaders to share wisdom for the next generation of salespreneurs.” — Branko Cerny, CEO/Cofounder of Immediately TINT has allowed #getunstuck to: Identify and increase engagement with members of top sales communities on Twitter, YouTube and LinkedIn. Aggregate and curate authentic video content to be shared and reshared on social media and participants’ social channels. Unite sales leaders around the world and to empower salespeople by joining the movement on www.getunstuck.club How #getunstuck used TINT: In order to use TINT, #getunstuck had to upload collected videos on an Youtube playlist called ‘getunstuck’. To connect the Youtube content, we utilized TINT to aggregate the private playlist and chose to we made them public. Controlling when the videos go live are valuable because a movement does not take place overnight. In order to create a movement, we had to command the release of the videos in a timely and strategic fashion. manually approve posts before Personally, I’ve been using Tint since 2013 and it has helped my projects save a total development cost of over $50k. TINT helps our project treat every social post like royalty W e’re excited to share the results in a follow-up post after the campaign is over so you can learn the best strategy! Join the movement today by uploading your community videos on @getunstuckdaily. Tell us how you #getunstuck in your day to day sales process. Twitter

TINT case study hacks — Sales Millennials’ #getunstuck campaign launches today

Back in 2010 when I started as the “analytics guy” at Demandware (which later became Salesforce Commerce Cloud), I worked with brands & retailers to help them identify growth opportunities using data. Back then, several of these organizations were still early in their e-commerce journeys. Traditional retail thinking had a heavy influence on the way digital leaders thought about business growth. This seeped into analytics and all things data. But Digital had this big advantage that everyone knew, but weren’t quite sure of how to leverage effectively — . data This led to organizational leaders behaving a bit like kids in a candy store - excited and acting “grabby” at any and all the data they can get. E-Commerce platforms were a firehose of customer behavior and transaction data, something that brick&mortar retail always struggled to collect. This created “fat data”. Data that accumulated in dashboards, emails, and slides that bogged leaders down. They were wading through “data muck” and more often than not, it sapped their energy instead of propelling them forward. Fast forward to 2023, as brands (are forced to) migrate over to (aka GA4), this “capture everything possible” mindset is making a comeback, albeit in a nuanced manner. It seems like Digital leaders are looking at this as an opportunity to refine their data capture strategy and fix things that they’ve seen break in their recent experience. Google Analytics 4 So I found myself asking - how do we help brand leaders understand what to track and get comfortable with that decision? Today, the attitude towards data has shifted a bit (for the better). E-commerce teams love data. Numbers, reports, and charts get emailed around and shared in collaboration tools on a regular basis. As a result, these organizations have internal “data factories” that churn out charts on a regular basis, for one-time studies as well as scheduled reporting. But is some amount of data collection unnecessary, and perhaps even damaging to the organization by creating complexity and adding confusion? Digital leaders want their decisions to be based on as much hard data as possible. Some still look at it as a source for reinforcing their intuition but some like to be challenged. In all cases, though, managers would do well to ask themselves about their data as a way to ensure that data moves the needle for the organization and doesn’t create an “analysis muck” for the leadership to wade through. 4 questions Are we asking the right questions? Many organizations just collect what’s available instead of focussing on the information needed to help make decisions and run the business. Instead, managers should make a list of questions they want data to answer. These questions should help them run the business and make operational decisions. Then focus their data collection efforts around those than everything else that’s possible. Does this data tell a story? Data in its is almost never usable. It needs to be put together into coherent information. This, then, needs to be connected to the current context of the business to tell a “story”. Data platforms and analytics systems are getting very good at the former. It seems like we still need a human for the latter. Generative AI may change that, but not in the summer of 2023. Managers should then focus on the data that’s needed to convey the story they want to tell. Stories sell. raw form Does this data help us look prepare by predicting probabilities? Data can help react to recent events, and it can also help prepare for potential events in the near future. Almost all tools these days provide some level of forecasting and predictive capabilities. As a last resort, doing simple regression in a spreadsheet tool of your choice is getting easier by the day. In this brave new world, managers should focus on collecting data for the metrics they want to be able to predict and prepare for. Does it help you answer the “why” behind events? We have gotten better are answering “what” questions. But not so much in answering “why”. Answers to “why” require some level of intuition and judgment. It also requires qualitative data. Managers would serve their organizations well by thinking in advance about qualitative data that can help answer the “why” behind the key questions they need to be answered. Collecting and processing data is only getting easier. But E-commerce leaders and managers need to be mindful of the operational burden and cognitive overload that data overload can create. Stay lean - move fast. Avoid data muck.

Do You Need All This Data?

Feature image created by Dall-E 2 Have you ever wondered what it’s like to chat with AI? I did, and I can tell you it was a chat-astrophe. I asked the AI how it felt, and it replied, ‘I feel great, thanks for asking. How can I help you with your existential crisis today?’ That’s the beauty of AI prompts: they can understand context and generate human responses that are sometimes too good to be true. AI Prompts Explained AI prompts are a powerful tool that uses (AI). They use machine learning algorithms to learn patterns and associations in data, allowing them to generate more human-like responses. They improve efficiency, help save costs and improve the customer experience. artificial intelligence AI prompts are growing in popularity because they can be applied everywhere — from customer service to education. How Does It Work AI prompts use (NLP) algorithms to respond based on a particular prompt or input. They learn through large data sets of text, such as social media or news articles. helps them to recognize patterns and associations in the data. Natural Language Processing Deep learning When a prompt is given, the AI prompt model uses the patterns it has learned to generate a response that is contextually relevant to the input. This process is called “inference” and calculates the probabilities of different word sequences based on the prompt and training data. The AI prompt model then selects the most likely word sequence and generates an answer similar to someone else’s. ChatGPT: The AI Prompt Model History and Development ChatGPT is one of the most popular AI prompt models. It was developed by OpenAI, a research organization whose goal is to create safe and useful AI. ChatGPT is built on the (GPT) architecture, which uses deep learning algorithms to generate natural language responses based on a given input or prompt. Generative Pre-trained Transformer ChatGPT aims to conduct conversations and has been trained in advance on a large corpus of conversation data. Since its launch, the model has become one of the most popular AI prompt models and is used in a variety of applications such as customer service, language translation, and creative writing. OpenAI has released several versions since the release of the GPT model, each with greater capacity and better performance. GPT-3 was launched in June 2020 and received a lot of attention for its impressive language skills. GPT-4 was released in March 2023 and ChatGPT is a specific application of the model trained only for conversational AI applications. By pre-training on a large amount of conversational data, it can generate responses that are contextually appropriate for the input. In a short time, ChatGPT has become one of the most popular prompt models. Using AI Prompts How AI Prompts Are Used Here are some examples of how AI prompts are used in different areas: : AI prompts are increasingly used in customer service to respond faster and more accurately to customer queries. For example, chatbots can be programmed with AI prompts to answer frequently asked questions, provide basic support, and even process orders. Customer service : AI prompts are used in healthcare to help doctors and nurses make more accurate diagnoses and treatment decisions. For example, AI prompts can be used to analyze patient data, suggest possible diagnoses, and recommend treatment options. Healthcare : In education, AI prompts are used to improve learning and student achievement. Education : AI prompts are used in finance to improve financial decision-making and reduce risk. Finance : In retail, AI prompts are used to improve customer experience and increase sales. AI prompts can also be used to analyze inventory levels. Retail AI prompts are being deployed across many different industries and applications to improve efficiency, accuracy, and personalization. Given the technological advancements in AI, we can expect more innovative applications of AI prompts in the near future. : Prompt to have your resume reviewed I want you to act as a resume reviewer. I will give you my resume and you give me feedback on it. Please add feedback on the following points: — What sections should I add to make my resume more readable. — How I can make my CV easier to scan. — What grades and results can I add to my CV. — What type of action verbs should I choose. — Am I using too much jargon and how to replace it. The Benefit of Using AI Prompts AI prompts provide several benefits to businesses and their customers. On the one hand, automating repetitive tasks, such as answering frequently asked questions, help. Thus, companies can improve efficiency. This can lead to faster response times and higher productivity. On the other hand, AI prompts provide a personalized interaction with the customer by offering suggestions based on preferences and behavior. In general, the use of AI prompts contributes to better efficiency and customer experience, which can ultimately contribute to the growing success of the Company. Ethical Considerations of AI Prompts Potential Ethical Issues The use of AI prompts raises several ethical issues. First, the results may be biased because the training data may contain information from a specific group or demographic, which in turn may lead to discriminatory outcomes. In addition, privacy can be violated by using information that can be traced back to individuals. It is therefore important that AI prompts are developed and used in an ethical manner, respecting the privacy of individuals and avoiding bias. How Are These Issues Addressed? Several initiatives are underway to address the ethical implications of using AI prompts. One way to do this is to improve the diversity and representativeness of the data used to train AI models. Another approach is to implement transparency and accountability measures so that the development and use of AI prompts are fair and equitable. For this, it is necessary to state the algorithms and data sources used, as well as implement control mechanisms to ensure that the results are fair and unbiased. Finally, there are efforts to promote privacy and data protection through the development of data protection laws and regulations, such as the (GDPR) in the European Union. General Data Protection Regulation These laws give individuals more control over their personal data and require organizations to take measures to protect the privacy rights of individuals. : To act as an educational content creator I want you to act as an educational content creator. You must create engaging and informative content for learning materials such as textbooks, online courses, and lecture notes. My first suggestion request is, “I need help developing a renewable energy lesson plan for high school students.” The Future of AI Prompts The future improvements in AI prompts will likely amount to an increase in their accuracy and the number of applications that become possible. One area that shows promise for improvement is the development of more advanced natural language processing (NLP) techniques. These can increase the precision of AI prompts in understanding and generating language. This would enable more complex and sensitive interactions with AI systems, which in turn could result in better and personalized answers to user questions. How to Create An AI Prompt For example, some tips to give ChatGPT the correct prompt: : Determine what you want to achieve with the prompt. Are you looking for a specific answer to a question, or are you looking for creative ideas or solutions to a problem? Determine your goal : Write your prompt in a clear and concise manner, using correct grammar and spelling. Avoid using ambiguous language or overly complex sentences that may confuse the model. Be clear and concise : Provide any relevant information or context that can help the model understand the task. This can be additional data or background information related to the topic of the prompt. Provide context : Don’t give up on a concept just because your first prompt didn’t produce the desired result. Often, finding the right word or wording can unlock what you’re looking for. Use a thesaurus : If you want the AI to understand your request, make sure your prompt contains a verb that properly expresses your intent. Watch your verbs : Make it clear from the start what you’re trying to do, and play with it. It is always helpful to place an umbrella of intent over what you want to do. Playing around with different ways to do that can make a big difference. ChatGPT is good at recognizing intentions : To act as title generator for articles I want you to act as an article title generator. I give you the topic and keywords of an article. You generate five catchy titles. Keep the titles concise and under 20 words and make sure to preserve the meaning. My first topic is “LearnData a knowledge base built on VuePress, into which I integrated all my notes and articles, making it easy to use and share.” In addition, it is useful to consider the limitations of the model and the type of responses it can generate. While ChatGPT is an advanced AI language model, it’s important to remember that it doesn’t always provide the exact answer you’re looking for and may require multiple prompts or prompt tweaks to get to the desired result. Ultimately, it is a statistical tool that gives the most plausible answer Conclusion AI prompts are an AI model that uses machine learning model algorithms so that responses can be generated in natural language. However, we cannot blindly assume that the responses are correct. Validation of the responses is still required. Sources (and Tips) for AI Prompts I’ve Used: Unfluffer ChatGPT DALL-E-2 10+ ChatGPT Prompts to Write Your Resume in a Blink of an Eye You’re Using ChatGPT Wrong! Here’s How to Be Ahead of 99% of ChatGPT Users How to Write Better ChatGPT Prompts, According to AI Engineer Awesome ChatGPT prompts 11 Cool ChatGPT Prompts for Data Science 73 Important ChatGPT Statistics & Facts for March 2023 (Gpt-4 Update) Timeline or ChatGPT I Paid a Professional to Edit a ChatGPT-Written Article — Hilarity Ensued ChatGPT Statistics 2023

What You Need to Know About AI Prompts

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Tensor & Flow: Part 1, TensorFlow & Machine Learning on Android

The fast and easy guide to the most popular Deep Learning framework in the world. Make sure to check out the other articles . here Overview In this installment we will be going over all the abstracted models that are currently available in TensorFlow and describe use cases for that particular model as well as simple sample code. Full sources of working examples are in the . TensorFlow In a Nutshell repo A recurrent neural network Recurrent Neural Networks Language Modeling, Machine translation, Word embedding, Text processing. Use Cases: Since the advent of Long Short Term Memory and Gated Recurrent Units, Recurrent Neural Networks have made leaps and bounds above other models in natural language processing. They can be fed vectors representing characters and be trained to generate new sentences based on the training set. The merit in this model is that it keeps the context of the sentence and derives meaning that “cat sat on the mat” means the cat is on the mat. Since the creation of TensorFlow writing these networks have become increasingly simpler. There are even hidden features covered by Denny Britz that make writing RNN’s even simpler heres a quick example. here import tensorflow as tfimport numpy as np X = np.random.randn(2, 10, 8) # Create input data X[1,6,:] = 0X_lengths = [10, 6] # The second example is of length 6 cell = tf.nn.rnn_cell.LSTMCell(num_units=64, state_is_tuple= )cell = tf.nn.rnn_cell.DropoutWrapper(cell=cell, output_keep_prob=0.5)cell = tf.nn.rnn_cell.MultiRNNCell(cells=[cell] * 4, state_is_tuple= ) True True outputs, last_states = tf.nn.dynamic_rnn(cell=cell,dtype=tf.float64,sequence_length=X_lengths,inputs=X) result = tf.contrib.learn.run_n({"outputs": outputs, "last_states": last_states},n=1,feed_dict= ) None Convolution Neural Network Convolution Neural Networks Image processing, Facial recognition, Computer Vision Use Cases: Convolution Neural Networks are unique because they’re created in mind that the input will be an image. CNNs perform a sliding window function to a matrix. The window is called a kernel and it slides across the image creating a convolved feature. from http://deeplearning.standford.edu/wiki/index.php/Feature_extraction_using_convolution Creating a convolved feature allows for edge detection which then allows for a to depict objects from pictures. network edge detection from GIMP manual The convolved feature to create this looks like this matrix below: Convolved feature from GIMP manual Here’s a sample of code to identify handwritten digits from the MNIST dataset. ### Convolutional networkdef max_pool_2x2(tensor_in):return tf.nn.max_pool(tensor_in, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') def conv_model(X, y): reshape X to 4d tensor with 2nd and 3rd dimensions being image width and height final dimension being the number of color channels. X = tf.reshape(X, [-1, 28, 28, 1]) first conv layer will compute 32 features for each 5x5 patch with tf.variable_scope('conv_layer1'):h_conv1 = learn.ops.conv2d(X, n_filters=32, filter_shape=[5, 5],bias=True, activation=tf.nn.relu)h_pool1 = max_pool_2x2(h_conv1) second conv layer will compute 64 features for each 5x5 patch. with tf.variable_scope('conv_layer2'):h_conv2 = learn.ops.conv2d(h_pool1, n_filters=64, filter_shape=[5, 5],bias=True, activation=tf.nn.relu)h_pool2 = max_pool_2x2(h_conv2) # reshape tensor into a batch of vectors  
h\_pool2\_flat = tf.reshape(h\_pool2, \[-1, 7 \* 7 \* 64\]) densely connected layer with 1024 neurons. h_fc1 = learn.ops.dnn(h_pool2_flat, [1024], activation=tf.nn.relu, dropout=0.5)return learn.models.logistic_regression(h_fc1, y) Feed Forward Neural Networks Classification and Regression Use Cases: These networks consist of perceptrons in layers that take inputs that pass information on to the next layer. The last layer in the network produces the output. There is no connection between each node in a given layer. The layer that has no original input and no final output is called the hidden layer. The goal of this network is similar to other supervised neural networks using back propagation, to make inputs have the desired trained outputs. These are some of the simplest effective neural networks for classification and regression problems. We will show how easy it is to create a feed forward network to classify handwritten digits: def init_weights(shape):return tf.Variable(tf.random_normal(shape, stddev=0.01)) def model(X, w_h, w_o):h = tf.nn.sigmoid(tf.matmul(X, w_h)) # this is a basic mlp, think 2 stacked logistic regressionsreturn tf.matmul(h, w_o) # note that we dont take the softmax at the end because our cost fn does that for us mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels X = tf.placeholder("float", [None, 784])Y = tf.placeholder("float", [None, 10]) w_h = init_weights([784, 625]) # create symbolic variablesw_o = init_weights([625, 10]) py_x = model(X, w_h, w_o) cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y)) # compute coststrain_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost) # construct an optimizerpredict_op = tf.argmax(py_x, 1) # Launch the graph in a sessionwith tf.Session() as sess:# you need to initialize all variablestf.initialize_all_variables().run() for i in range(100):for start, end in zip(range(0, len(trX), 128), range(128, len(trX)+1, 128)):sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})print(i, np.mean(np.argmax(teY, axis=1) ==sess.run(predict_op, feed_dict={X: teX, Y: teY}))) Linear Models Classification and Regression Use Cases: Linear models take X values and produce a line of best fit used for classification and regression of Y values. For example if you have a list of house sizes and their price in a neighborhood you can predict the price of house given the size using a linear model. One thing to note is that linear models can be used for multiple X features. For example in the housing example we can create a linear model given house sizes, how many rooms, how many bathrooms and price and predict price given a house with size, # of rooms, # of bathrooms. import numpy as npimport tensorflow as tf def weight_variable(shape):initial = tf.truncated_normal(shape, stddev=1)return tf.Variable(initial) # datasetxx = np.random.randint(0,1000,[1000,3])/1000.yy = xx[:,0] * 2 + xx[:,1] * 1.4 + xx[:,2] * 3 # modelx = tf.placeholder(tf.float32, shape=[None, 3])y_ = tf.placeholder(tf.float32, shape=[None])W1 = weight_variable([3, 1])y = tf.matmul(x, W1) # training and cost functioncost_function = tf.reduce_mean(tf.square(tf.squeeze(y) - y_))train_function = tf.train.AdamOptimizer(1e-2).minimize(cost_function) # create a sessionsess = tf.Session() # trainsess.run(tf.initialize_all_variables())for i in range(10000):sess.run(train_function, feed_dict={x:xx, y_:yy})if i % 1000 == 0:print(sess.run(cost_function, feed_dict={x:xx, y_:yy})) Support Vector Machines Currently only Binary Classification Use Cases: The general idea behind a SVM is that there is an optimal hyperplane for linearly separable patterns. For data that is not linearly separable we can use a kernel function to transform the original data into a new space. SVMs maximize the margin around separating the hyperplane. They work extremely well in high dimensional spaces and and are still effective if the dimensions are greater than the number of samples. def input_fn():return {'example_id': tf.constant(['1', '2', '3']),'price': tf.constant([[0.6], [0.8], [0.3]]),'sq_footage': tf.constant([[900.0], [700.0], [600.0]]),'country': tf.SparseTensor(values=['IT', 'US', 'GB'],indices=[[0, 0], [1, 3], [2, 1]],shape=[3, 5]),'weights': tf.constant([[3.0], [1.0], [1.0]])}, tf.constant([[1], [0], [1]]) price = tf.contrib.layers.real_valued_column('price')sq_footage_bucket = tf.contrib.layers.bucketized_column(tf.contrib.layers.real_valued_column('sq_footage'),boundaries=[650.0, 800.0])country = tf.contrib.layers.sparse_column_with_hash_bucket('country', hash_bucket_size=5)sq_footage_country = tf.contrib.layers.crossed_column([sq_footage_bucket, country], hash_bucket_size=10)svm_classifier = tf.contrib.learn.SVM(feature_columns=[price, sq_footage_bucket, country, sq_footage_country],example_id_column='example_id',weight_column_name='weights',l1_regularization=0.1,l2_regularization=1.0) svm_classifier.fit(input_fn=input_fn, steps=30)accuracy = svm_classifier.evaluate(input_fn=input_fn, steps=1)['accuracy'] Deep and Wide Models Recommendation systems, Classification and Regression Use Cases: Deep and Wide models were covered with greater detail in , so we won’t get too heavy here. A Wide and Deep Network combines a linear model with a feed forward neural net so that our predictions will have memorization and generalization. This type of model can be used for classification and regression problems. This allows for less feature engineering with relatively accurate predictions. Thus, getting the best of both worlds. Here’s a code snippet from . part two part two’s github def input_fn(df, train=False):"""Input builder function.""" Creates a dictionary mapping from each continuous feature column name (k) to the values of that column stored in a constant Tensor. continuous_cols = {k: tf.constant(df[k].values) for k in CONTINUOUS_COLUMNS} Creates a dictionary mapping from each categorical feature column name (k) to the values of that column stored in a tf.SparseTensor. categorical_cols = {k: tf.SparseTensor(indices=[[i, 0] for i in range(df[k].size)],values=df[k].values,shape=[df[k].size, 1])for k in CATEGORICAL_COLUMNS} Merges the two dictionaries into one. feature_cols = dict(continuous_cols)feature_cols.update(categorical_cols) Converts the label column into a constant Tensor. if train:label = tf.constant(df[SURVIVED_COLUMN].values)# Returns the feature columns and the label.return feature_cols, labelelse:return feature_cols m = build_estimator(model_dir)m.fit(input_fn=lambda: input_fn(df_train, True), steps=200)print m.predict(input_fn=lambda: input_fn(df_test))results = m.evaluate(input_fn=lambda: input_fn(df_train, True), steps=1)for key in sorted(results):print("%s: %s" % (key, results[key])) Random Forest Classification and Regression Use Cases: Random Forest model takes many different classification trees and each tree votes for that class. The forest chooses the classification having the most votes. Random Forests do not overfit, you can run as many treees as you want and it is relatively fast. Give it a try on the iris data with this snippet below: hparams = tf.contrib.tensor_forest.python.tensor_forest.ForestHParams(num_trees=3, max_nodes=1000, num_classes=3, num_features=4)classifier = tf.contrib.learn.TensorForestEstimator(hparams) iris = tf.contrib.learn.datasets.load_iris()data = iris.data.astype(np.float32)target = iris.target.astype(np.float32) monitors = [tf.contrib.learn.TensorForestLossMonitor(10, 10)]classifier.fit(x=data, y=target, steps=100, monitors=monitors)classifier.evaluate(x=data, y=target, steps=10) Bayesian Reinforcement Learning Classification and Regression Use Cases: In the contrib folder of TensorFlow there is a library called BayesFlow. BayesFlow has no documentation except for an example of the REINFORCE algorithm. This algorithm is proposed in a by Ronald Williams. paper ward ncrement = onnegative actor * ffset einforcement * haracteristic ligibility RE I N F O R C E This network trying to solve an immediate reinforcement task, adjusts the weights after getting the reinforcement value at each trial. At the end of each trial each weight is incremented by a learning rate factor multiplied by the reinforcement value minus the baseline multiplied by characteristic eligibility. Williams paper also discusses the use of back propagation to train the REINFORCE network. learning """Build the Split-Apply-Merge Model.Route each value of input [-1, -1, 1, 1] through one of thefunctions, plus_1, minus_1.  The decision for routing is made by4 Bernoulli R.V.s whose parameters are determined by a neural networkapplied to the input.  REINFORCE is used to update the NN parameters.Returns:The 3-tuple (route_selection, routing_loss, final_loss), where:- route_selection is an int 4-vector- routing_loss is a float 4-vector- final_loss is a float scalar."""inputs = tf.constant([[-1.0], [-1.0], [1.0], [1.0]])targets = tf.constant([[0.0], [0.0], [0.0], [0.0]])paths = [plus_1, minus_1]weights = tf.get_variable("w", [1, 2])bias = tf.get_variable("b", [1, 1])logits = tf.matmul(inputs, weights) + bias # REINFORCE forward steproute_selection = st.StochasticTensor(distributions.Categorical, logits=logits) Linear Chain Conditional Random Fields Sequential Data Use Cases: CRFs are conditional probability distributions that factoirze according to an undirected model. They predict a label for a single sample keeping context from the neighboring samples. CRFs are similar to Hidden Markov Models. CRFs are often used for image segmentation and object recognition, as well as shallow parsing, named entity recognition and gene finding. # Train for a fixed number of iterations.session.run(tf.initialize_all_variables())for i in range(1000):tf_unary_scores, tf_transition_params, _ = session.run([unary_scores, transition_params, train_op])if i % 100 == 0:correct_labels = 0total_labels = 0for tf_unary_scores_, y_, sequence_length_ in zip(tf_unary_scores, y, sequence_lengths):# Remove padding from the scores and tag sequence.tf_unary_scores_ = tf_unary_scores_[:sequence_length_]y_ = y_[:sequence_length_] # Compute the highest scoring sequence.  
    viterbi\_sequence, \_ = tf.contrib.crf.viterbi\_decode(  
        tf\_unary\_scores\_, tf\_transition\_params)  

    # Evaluate word-level accuracy.  
    correct\_labels += np.sum(np.equal(viterbi\_sequence, y\_))  
    total\_labels += sequence\_length\_  
  accuracy = 100.0 \* correct\_labels / float(total\_labels)  
  print("Accuracy: %.2f%%" % accuracy) Conclusion Ever since TensorFlow has been released the community surrounding the project has been adding more packages, examples and cases for using this amazing library. Even at the time of writing this article there are more models and sample code being written. It is amazing to see how much TensorFlow as grown in these past few months. The ease of use and diversity in the package are increasing overtime and don’t seem to be slowing down anytime soon. As always — Feel free to email me any questions or inquiries at camron@camron.xyz Originally posted at Camron.xyz

TensorFlow in a Nutshell — Part Three: All the Models

Authors:
(1) Kexun Zhang, UC Santa Barbara and Equal contribution;
(2) Hongqiao Chen, Northwood High School and Equal contribution;
(3) Lei Li, Carnegie Mellon University;
(4) William Yang Wang,UC Santa Barbara. Table of Links Abstract and Intro
Related Work
ToolDec: LLM Tool Use via Finite-State Decoding
Experiment: ToolDec Eliminates Syntax Errors
Experiment: ToolDec Enables Generalizable Tool Selection
Conclusion and References
Appendix 6. CONCLUSION This paper presents TOOLDEC, a novel decoding algorithm designed to enhance Large Language Models (LLMs) by integrating external tools and ensuring their invocation is syntax-error-free. TOOLDEC, guided by a finite-state machine constructed from tool documentation and API signatures, accurately represents the grammar of tool calls, addressing prevalent issues like erroneous tool calls and poor generalization to unseen tools in existing models. Experiments demonstrate that TOOLDEC eliminates tool-related syntax errors, improves accuracy, and saves inference time across various benchmarks. It also exhibits the ability to generalize to unseen tools in multiple domains without additional fine-tuning data. The advancements by TOOLDEC open avenues for research in developing more sophisticated models adaptable to a wider range of tools and applications without additional training data, leading to more versatile and robust LLMs capable of solving a broader spectrum of complex problems. The success of TOOLDEC in eliminating syntax errors can inspire research focusing on semantic accuracy and contextual relevance of tool calls. This can lead to models that invoke, understand, and leverage tools more effectively, enhancing LLMs’ overall problem-solving capabilities. REFERENCES Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. Guided open vocabulary image captioning with constrained beam search. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 936–945, Copenhagen, Denmark, September 2017. Association for Computational Linguistics. doi: 10.18653/v1/D17-1098. URL https://aclanthology.org/D17-1098. Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al. Improving language models by retrieving from trillions of tokens. In International conference on machine learning, pp. 2206–2240. PMLR, 2022. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020. Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen. Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks. arXiv e-prints, pp. arXiv–2211, 2022. Jason Eisner. Parameter estimation for probabilistic finite-state transducers. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 1–8, 2002. Edward Fredkin. Trie memory. Communications of the ACM, 3(9):490–499, 1960. Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. Pal: Program-aided language models. In International Conference on Machine Learning, pp. 10764–10799. PMLR, 2023. Tanmay Gupta and Aniruddha Kembhavi. Visual programming: Compositional visual reasoning without training. ArXiv, abs/2211.11559, 2022. Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. Retrieval augmented language model pre-training. In International conference on machine learning, pp. 3929–3938. PMLR, 2020. Shibo Hao, Tianyang Liu, Zhen Wang, and Zhiting Hu. Toolkengpt: Augmenting frozen language models with massive tools via tool embeddings. arXiv preprint arXiv:2305.11554, 2023. Chris Hokamp and Qun Liu. Lexically constrained decoding for sequence generation using grid beam search. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1535–1546, Vancouver, Canada, July 2017. Association for Computational Linguistics. doi: 10.18653/v1/P17-1141. URL https:// aclanthology.org/P17-1141. Jan-Christoph Kalo and Leandra Fichtel. Kamel : Knowledge analysis with multitoken entities in language models. Automated Knowledge Base Construction, 2022. Li Li, Wu Chou, Wei Zhou, and Min Luo. Design patterns and extensibility of rest api for networking applications. IEEE Transactions on Network and Service Management, 13(1):154–167, 2016. Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. Neurologic decoding:(un) supervised neural text generation with predicate logic constraints. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4288–4299, 2021. Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, et al. Neurologic a* esque decoding: Constrained text generation with lookahead heuristics. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 780–799, 2022. Gregoire Mialon, Roberto Dess ´ `ı, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Roziere, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al. Augmented ` language models: a survey. arXiv preprint arXiv:2302.07842, 2023. Ning Miao, Hao Zhou, Lili Mou, Rui Yan, and Lei Li. Cgmh: Constrained sentence generation by metropolis-hastings sampling. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pp. 6834–6842, 2019. Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al. Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332, 2021. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730–27744, 2022. Aaron Parisi, Yao Zhao, and Noah Fiedel. Talm: Tool augmented language models. arXiv preprint arXiv:2205.12255, 2022. Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al. Toolllm: Facilitating large language models to master 16000+ real-world apis. arXiv preprint arXiv:2307.16789, 2023. Pushpendre Rastogi, Ryan Cotterell, and Jason Eisner. Weighting finite-state transductions with neural context. In Proceedings of the 2016 conference of the North American chapter of the Association for Computational Linguistics: human language technologies, pp. 623–633, 2016. Timo Schick, Jane Dwivedi-Yu, Roberto Dess`ı, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761, 2023. Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face, 2023. Yifan Song, Weimin Xiong, Dawei Zhu, Wenhao Wu, Han Qian, Mingbo Song, Hailiang Huang, Cheng Li, Ke Wang, Rong Yao, Ye Tian, and Sujian Li. Restgpt: Connecting large language models with real-world restful apis, 2023. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothee´ Lacroix, Baptiste Roziere, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Ar- ` mand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models, 2023. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models, 2023. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. Judging llm-as-a-judge with mt-bench and chatbot arena, 2023. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Kexun Zhang, UC Santa Barbara and Equal contribution; (2) Hongqiao Chen, Northwood High School and Equal contribution; (3) Lei Li, Carnegie Mellon University; (4) William Yang Wang,UC Santa Barbara. Authors: Authors: (1) Kexun Zhang, UC Santa Barbara and Equal contribution; (2) Hongqiao Chen, Northwood High School and Equal contribution; (3) Lei Li, Carnegie Mellon University; (4) William Yang Wang,UC Santa Barbara. Table of Links Abstract and Intro Related Work ToolDec: LLM Tool Use via Finite-State Decoding Experiment: ToolDec Eliminates Syntax Errors Experiment: ToolDec Enables Generalizable Tool Selection Conclusion and References Appendix Abstract and Intro Abstract and Intro Related Work Related Work ToolDec: LLM Tool Use via Finite-State Decoding ToolDec: LLM Tool Use via Finite-State Decoding Experiment: ToolDec Eliminates Syntax Errors Experiment: ToolDec Eliminates Syntax Errors Experiment: ToolDec Enables Generalizable Tool Selection Experiment: ToolDec Enables Generalizable Tool Selection Conclusion and References Conclusion and References Appendix Appendix 6. CONCLUSION This paper presents TOOLDEC, a novel decoding algorithm designed to enhance Large Language Models (LLMs) by integrating external tools and ensuring their invocation is syntax-error-free. TOOLDEC, guided by a finite-state machine constructed from tool documentation and API signatures, accurately represents the grammar of tool calls, addressing prevalent issues like erroneous tool calls and poor generalization to unseen tools in existing models. Experiments demonstrate that TOOLDEC eliminates tool-related syntax errors, improves accuracy, and saves inference time across various benchmarks. It also exhibits the ability to generalize to unseen tools in multiple domains without additional fine-tuning data. The advancements by TOOLDEC open avenues for research in developing more sophisticated models adaptable to a wider range of tools and applications without additional training data, leading to more versatile and robust LLMs capable of solving a broader spectrum of complex problems. The success of TOOLDEC in eliminating syntax errors can inspire research focusing on semantic accuracy and contextual relevance of tool calls. This can lead to models that invoke, understand, and leverage tools more effectively, enhancing LLMs’ overall problem-solving capabilities. REFERENCES Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. Guided open vocabulary image captioning with constrained beam search. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 936–945, Copenhagen, Denmark, September 2017. Association for Computational Linguistics. doi: 10.18653/v1/D17-1098. URL https://aclanthology.org/D17-1098. Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al. Improving language models by retrieving from trillions of tokens. In International conference on machine learning, pp. 2206–2240. PMLR, 2022. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020. Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen. Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks. arXiv e-prints, pp. arXiv–2211, 2022. Jason Eisner. Parameter estimation for probabilistic finite-state transducers. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 1–8, 2002. Edward Fredkin. Trie memory. Communications of the ACM, 3(9):490–499, 1960. Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. Pal: Program-aided language models. In International Conference on Machine Learning, pp. 10764–10799. PMLR, 2023. Tanmay Gupta and Aniruddha Kembhavi. Visual programming: Compositional visual reasoning without training. ArXiv, abs/2211.11559, 2022. Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. Retrieval augmented language model pre-training. In International conference on machine learning, pp. 3929–3938. PMLR, 2020. Shibo Hao, Tianyang Liu, Zhen Wang, and Zhiting Hu. Toolkengpt: Augmenting frozen language models with massive tools via tool embeddings. arXiv preprint arXiv:2305.11554, 2023. Chris Hokamp and Qun Liu. Lexically constrained decoding for sequence generation using grid beam search. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1535–1546, Vancouver, Canada, July 2017. Association for Computational Linguistics. doi: 10.18653/v1/P17-1141. URL https:// aclanthology.org/P17-1141. Jan-Christoph Kalo and Leandra Fichtel. Kamel : Knowledge analysis with multitoken entities in language models. Automated Knowledge Base Construction, 2022. Li Li, Wu Chou, Wei Zhou, and Min Luo. Design patterns and extensibility of rest api for networking applications. IEEE Transactions on Network and Service Management, 13(1):154–167, 2016. Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. Neurologic decoding:(un) supervised neural text generation with predicate logic constraints. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4288–4299, 2021. Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, et al. Neurologic a* esque decoding: Constrained text generation with lookahead heuristics. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 780–799, 2022. Gregoire Mialon, Roberto Dess ´ `ı, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Roziere, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al. Augmented ` language models: a survey. arXiv preprint arXiv:2302.07842, 2023. Ning Miao, Hao Zhou, Lili Mou, Rui Yan, and Lei Li. Cgmh: Constrained sentence generation by metropolis-hastings sampling. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pp. 6834–6842, 2019. Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al. Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332, 2021. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730–27744, 2022. Aaron Parisi, Yao Zhao, and Noah Fiedel. Talm: Tool augmented language models. arXiv preprint arXiv:2205.12255, 2022. Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al. Toolllm: Facilitating large language models to master 16000+ real-world apis. arXiv preprint arXiv:2307.16789, 2023. Pushpendre Rastogi, Ryan Cotterell, and Jason Eisner. Weighting finite-state transductions with neural context. In Proceedings of the 2016 conference of the North American chapter of the Association for Computational Linguistics: human language technologies, pp. 623–633, 2016. Timo Schick, Jane Dwivedi-Yu, Roberto Dess`ı, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761, 2023. Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face, 2023. Yifan Song, Weimin Xiong, Dawei Zhu, Wenhao Wu, Han Qian, Mingbo Song, Hailiang Huang, Cheng Li, Ke Wang, Rong Yao, Ye Tian, and Sujian Li. Restgpt: Connecting large language models with real-world restful apis, 2023. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothee´ Lacroix, Baptiste Roziere, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Ar- ` mand Joulin, Edouard Grave, and Guillaume Lample. Llama: Open and efficient foundation language models, 2023. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models, 2023. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. Judging llm-as-a-judge with mt-bench and chatbot arena, 2023. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv

Syntax Error-Free and Generalizable Tool Use for LLMs: Conclusion and References

এলএলএম-এর জন্য সিনট্যাক্স ত্রুটি-মুক্ত এবং সাধারণীকরণযোগ্য টুল ব্যবহার: উপসংহার এবং রেফারেন্স

Syntaxfehlerfreier und generalisierbarer Tooleinsatz für LL.M.: Fazit und Referenzen

Uso de herramientas generalizables y sin errores de sintaxis para LLM: conclusiones y referencias

Utilisation d'outils de syntaxe sans erreur et généralisables pour les LLM : conclusion et références

एलएलएम के लिए वाक्यविन्यास त्रुटि-मुक्त और सामान्यीकरण योग्य उपकरण का उपयोग: निष्कर्ष और संदर्भ

LLM における構文エラーのない一般化可能なツールの使用: 結論と参考文献

LLM을 위한 구문 오류가 없고 일반화 가능한 도구 사용: 결론 및 참조

Uso de ferramenta generalizável e livre de erros de sintaxe para LLMs: conclusão e referências

Использование без синтаксических ошибок и обобщения инструментов для программ LLM: выводы и ссылки

Yüksek Lisans'lar için Sözdizimi Hatasız ve Genelleştirilebilir Araç Kullanımı: Sonuç ve Referanslar

Sử dụng công cụ không có lỗi và có thể khái quát hóa cho LLM: Kết luận và tài liệu tham khảo

语法无错误且可推广的法学硕士工具使用：结论和参考文献

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VEATIC: परिचितता और आनंद रेटिंग और संदर्भ

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Async/await in C# is a framework used for writing asynchronous C# code that is both readable and maintainable. These tips will help you to integrate async/await programming more effectively in the # projects: 1. ValueTask for Lightweight Operations Use ValueTask instead of Task for asynchronous methods that often complete synchronously, reducing the allocation overhead. public async ValueTask<int> GetResultAsync()
    {
        if (cachedResult != null)
            return cachedResult;
        int result = await ComputeResultAsync();
        cachedResult = result;
        return result;
    } 2. ConfigureAwait for Library Code Use ConfigureAwait(false) in the library code to avoid deadlocks by not capturing the synchronization context. public async Task SomeLibraryMethodAsync()
    {
        await SomeAsyncOperation().ConfigureAwait(false);
    } 3. Avoiding async void Prefer async Task over async void except for event handlers, as async void can lead to unhandled exceptions and is harder to test. public async Task EventHandlerAsync(object sender, EventArgs e)
    {
        await PerformOperationAsync();
    } 4. Using IAsyncDisposable For asynchronous cleanup, implement IAsyncDisposable and use await to ensure resources are released properly. public async ValueTask DisposeAsync()
    {
        await DisposeAsyncCore();
    }
    
    private async ValueTask DisposeAsyncCore()
    {
        if (resource != null)
        {
            await resource.DisposeAsync();
        }
    } 5. Efficiently Combine Tasks Use Task.WhenAll for running multiple tasks in parallel and waiting for all to complete, which is more efficient than awaiting each task sequentially public async Task ProcessTasksAsync()
    {
        Task task1 = DoTask1Async();
        Task task2 = DoTask2Async();
        await Task.WhenAll(task1, task2);
    } 6. Cancellation Support Support cancellation in asynchronous methods using CancellationToken. public async Task DoOperationAsync(CancellationToken cancellationToken)
    {
        await LongRunningOperationAsync(cancellationToken);
        cancellationToken.ThrowIfCancellationRequested();
    } 7. State Machine Optimization For performance-critical code, consider structuring your async methods to minimize the creation of state machines by separating synchronous and asynchronous paths. public async Task<int> FastPathAsync()
    {
        if (TryGetCachedResult(out int result))
        {
            return result;
        }
        return await ComputeResultAsync();
    } 8. Avoid Blocking Calls Avoid blocking on async code with .Result or .Wait(). Instead, use asynchronous waiting through the stack. public async Task WrapperMethodAsync()
    {
        int result = await GetResultAsync();
    } 9. Eliding Async/Await In simple passthrough scenarios or when returning a task directly, you can elide the async and await keywords for slightly improved performance. public Task<int> GetResultAsync() => ComputeResultAsync(); 10. Custom Task Schedulers For advanced scenarios, like limiting concurrency or capturing synchronization contexts, consider implementing a custom TaskScheduler. public sealed class LimitedConcurrencyLevelTaskScheduler : TaskScheduler
    {
        // Implement the scheduler logic here.
    } 11. Using Asynchronous Streams Leverage asynchronous streams with IAsyncEnumerable<T> for processing sequences of data asynchronously, introduced in C# 8.0. public async IAsyncEnumerable<int> GetNumbersAsync()
    {
        for (int i = 0; i < 10; i++)
        {
            await Task.Delay(100); // Simulate async work
            yield return i;
        }
    } 12. Avoid Async Lambdas in Hot Paths Async lambdas can introduce overhead. In performance-critical paths, consider refactoring them into separate async methods. // Before optimization
    Func<Task> asyncLambda = async () => await DoWorkAsync();
    
    // After optimization
    public async Task DoWorkMethodAsync()
    {
        await DoWorkAsync();
    } 13. Use SemaphoreSlim for Async Coordination SemaphoreSlim can be used for async coordination, such as limiting access to a resource in a thread-safe manner. private readonly SemaphoreSlim semaphore = new SemaphoreSlim(1, 1);
    
    public async Task UseResourceAsync()
    {
        await semaphore.WaitAsync();
        try
        {
            // Access the resource
        }
        finally
        {
            semaphore.Release();
        }
    } 14. Task.Yield for UI Responsiveness Use await Task.Yield() in UI applications to ensure the UI remains responsive by allowing other operations to process. public async Task LoadDataAsync()
    {
        await Task.Yield(); // Return control to the UI thread
        // Load data here
    } 15. Asynchronous Lazy Initialization Use Lazy<Task<T>> for asynchronous lazy initialization, ensuring the initialization logic runs only once and is thread-safe. private readonly Lazy<Task<MyObject>> lazyObject = new Lazy<Task<MyObject>>(async () =>
    {
        return await InitializeAsync();
    });
    
    public async Task<MyObject> GetObjectAsync() => await lazyObject.Value; 16. Combining async and LINQ Be cautious when combining async methods with LINQ queries; consider using asynchronous streams or explicitly unwrapping tasks when necessary. public async Task<IEnumerable<int>> ProcessDataAsync()
    {
        var data = await GetDataAsync(); // Assume this returns Task<List<int>>
        return data.Where(x => x > 10);
    } 17. Error Handling in Async Streams Handle errors gracefully in asynchronous streams by encapsulating the yielding loop in a try-catch block. public async IAsyncEnumerable<int> GetNumbersWithErrorsAsync()
    {
        try
        {
            for (int i = 0; i < 10; i++)
            {
                if (i == 5) throw new InvalidOperationException("Test error");
                yield return i;
            }
        }
        catch (Exception ex)
        {
            // Handle or log the error
        }
    } 18. Use Parallel.ForEachAsync for Asynchronous Parallel Loops Utilize Parallel.ForEachAsync in .NET 6 and later for running asynchronous operations in parallel, providing a more efficient way to handle CPU-bound and I/O-bound operations concurrently. await Parallel.ForEachAsync(data, async (item, cancellationToken) =>
    {
        await ProcessItemAsync(item, cancellationToken);
    }); 22. Avoid Excessive Async/Await in High-Performance Code In performance-critical sections, minimize the use of async/await. Instead, consider using Task.ContinueWith with caution or redesigning the workflow to reduce asynchronous calls. Task<int> task = ComputeAsync();
    task.ContinueWith(t => Process(t.Result)); 23. Async Main Method Utilize the async entry point for console applications introduced in C# 7.1 to simplify initialization code. public static async Task Main(string[] args)
    {
        await StartApplicationAsync();
    } 24. Optimize Async Loops For loops performing asynchronous operations, consider batching or parallelizing tasks to improve throughput. var tasks = new List<Task>();
    for (int i = 0; i < items.Length; i++)
    {
        tasks.Add(ProcessItemAsync(items[i]));
    }
    await Task.WhenAll(tasks); More Cheatsheets Cheat Sheets - .Net C# Programming🚀 Thank you for being a part of the C# community! Before you leave: Follow us: | | | Visit our other platforms: More content at Youtube X LinkedIn Dev.to GitHub C# Programming Also published . here

Async/Await For Basic to Advanced C# Developers: 24 Essential Tips

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