The company&#x27;s innovation in Bert Nova, which focuses on business continuity, suggests that it is actively developing new products and enhancing its offerings in the industrial energy efficiency sector. Furthermore, Bert Labs&#x27; role in driving digital transformation, a key area within the information ...

Bert Labs - Crunchbase Company Profile & Funding

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Bert Labs

It motivates us to keep pushing boundaries and creating a more sustainable, tech-powered world. #Innovation #Sustainability #TechForGood #BertLabs #nasscomemerge50 #nasscom #AcceleratingDeepTechProductGrowth #Emerge50 #Emerge50Awards. nasscom | nasscom deeptech | nasscom startups | Rohit Kochar ...

Bert Labs | LinkedIn

Bert Labs (www.bertlabs.com) formally started early 2017. We&#x27;ve built Artificial Intelligence - Internet of Things powered Platform Solution for Energy Efficiency Improvement, Production Efficiency Improvement, for Factories, Manufacturing Plants, Production Facilities, R&amp;D Facilities and ...

Bert Labs Company Profile, information, investors, valuation & Funding

Information on valuation, funding, cap tables, investors, and executives for Bert Labs. Use the PitchBook Platform to explore the full profile.

Bert Labs Company Profile: Valuation & Investors | PitchBook

Bert Labs - Provider of IoT products and digital twin software. Raised a total funding of $1.44M over 5 rounds from 21 investors.

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Bert Labs engages in designing, developing products, artificial intelligence, and IoT services. Use the CB Insights Platform to explore Bert Labs&#x27;s full profile.

Bert Labs - Products, Competitors, Financials, Employees, ...

Bert Labs Private Limited&#x27;s Corporate ... is rohit@bertlabs.com and its registered address is C1 - 906, L&amp;T South City, Bannerghatta Road, NA Bangalore Bangalore KA 560076 IN. Current status of Bert Labs Private Limited is - Active. Click here to view all available financial documents and regulatory filings of BERT LABS PRIVATE LIMITED ... Website: Click here to add. ... *Industry classification is derived from National Industrial Classification. If the company has changed ...

BERT LABS PRIVATE LIMITED - Company, directors and contact details ...

The company designed several products for US &amp; Indian markets. Working as Senior Research engineer where I do both both Hardware &amp; Firmware ... Previously Co-Founder@ Vectorly. Lead Researcher @ BERTLABS. Studied @ IIT Delhi. Tech enthusiast, Interested in Solving the worlds biggest problems ...

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See what employees say it&#x27;s like to work at Bert Labs. Salaries, reviews, and more - all posted by employees working at Bert Labs.

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Bert Labs Company Profile & Overview | AmbitionBox

We focus on building new technology ... &amp; predictive maintenance. Bert Labs would be US$2million revenue company year 1, US$100 million year 5, and US$1billion year 8. ... At Bert Labs (https://www.bertlabs....

Bert Labs Jobs | AngelList Talent

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Companies directory | SignalHire

Bert Labs is making things connected for consumers, businesses, and governments. We are conceptualizing, designing and developing products, leveraging Artificial Intelligence, and Internet of Things, to provide a solution framework that will make our customers cost effective and improve the experience of our customer's customer. We allow customers to leverage our software and hardware to generate customized applications, delivered within the nerves of where they are located.

Generative AI is a type of machine learning and artificial intelligence (AI) that is garnering a lot of attention right now, and we want to help keep you a step ahead by sharing the latest training available from Google Cloud. In this blog, we have compiled resources to help you learn about generative AI, how it can impact your business, and how to use it. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here. We have brand-new generative AI training options, and are constantly adding to our training catalog on Google Cloud Skills Boost. This includes two learning paths that each feature comprehensive content: one is for the non-technical audience (introductory) ‘Introduction to Generative AI learning path’, and the other is for technical practitioners (more advanced) ‘Generative AI for Developers learning path’. Prefer less of a time commitment? Opt for individual courses and labs with the topics that suit your specific interest. Powered by Google Cloud technology, generative AI is reimagining the way that organizations do business by leveraging data. The use cases are far-ranging, from having smarter conversations with and creating rich experiences for customers, to enhancing business processes, to helping developers write code, and more. Google Cloud has been working for decades to bring AI technology solutions to organizations, and our tools make it easier to build experiences across our cloud portfolio. Whether you’re executive-level, an IT decision maker, in a non-technical role, or a technical practitioner, we have videos, courses and labs to help you learn about the power of generative AI. Plus, labs give you direct access to Generative AI Studio and Vertex AI (Google Cloud’s machine learning platform) to get hands-on, technical experience. Get started with our training list below. No-cost foundational courses: no technical experience needed Don’t have a lot of time but want to learn the basics? Our no-cost Introduction to Generative AI course is a great place to start. It takes about 45 minutes to finish. If getting started has you hungry to learn more, continue your no-cost learning to earn the Generative AI Fundamentals skill badge. It includes the course above, plus two other courses: ‘Introduction to Large Language Models’ and ‘Introduction to Responsible AI’. In total, this takes about two hours to complete and you’ll earn a digital skill badge to share on your resume and social channels to validate your foundational understanding of Google Cloud generative AI technology. Dig in deeper with our generative AI learning paths For a more comprehensive learning experience, choose from our two new gen AI learning paths — a mix of courses, labs and skill badges. Please note: we plan to add new trainings to this list, so keep checking back for more. Introduction to Generative AI - this path includes no-cost video courses. Content is non-technical, introductory, and designed for the tech-adjacent audience: roles like sales, HR, marketing and operations. Here is a look at the content: Introduction to Generative AI - course
Introduction to Large Language Models - course
Introduction to Responsible AI - course
Generative AI Fundamentals - earn a skill badge
Responsible AI: Applying AI Principals with Google Cloud - course Generative AI for Developers - this path includes a combination of technical hands-on labs and courses and requires Google Cloud credits to complete. It is designed for technical developers who work with gen AI, and includes the introductory training as a prerequisite. Content includes: Responsible AI for Developers - course
Introduction to Image Generation - course
Attention Mechanism - course
Encoder-Decoder Architecture - course
Transformer Models and BERT Model - course
Create Image Captioning Models - course
Introduction to Generative AI Studio - course
Generative AI Explorer: Vertex AI - earn a skill badge
Explore and Evaluate Models Using Model Garden - lab
Prompt Design Using PaLM - lab Gain technical experience with labs We currently offer these generative AI labs for anyone looking to get some hands-on experience by working directly in Google Cloud: Get Started with Generative AI Studio (requires one credit) - introductory, 60 minutes
Generative AI with Vertex AI: Getting Started (requires one credit) - introductory, 90 minutes
Generative AI with Vertex AI: Prompt Design (requires one credit) - introductory, 90 minutes
Vertex AI PaLM API: Qwik Start (requires one credit) - introductory, 60 minutes
Integrate Search in Applications Using Gen AI App Builder (requires five credits) - introductory, 90 minutes
Prompt Design using PaLM (requires one credit)  - intermediate, 90 minutes
Explore and Evaluate Models using Model Garden (requires five credits) - intermediate, 60 minutes Text embeddings with DeepLearning.AI and Andrew Ng We have brand new content built in collaboration with DeepLearning.AI, taught by Andrew Ng, founder of DeepLearning.AI and a globally recognized leader on the topic. Enroll for no-cost in “Understanding and Applying Text Embeddings with Vertex AI” to learn various tasks like classification, outlier detection, text clustering, and semantic search. You’ll also learn to integrate them into LLM applications. Check out the video below to hear about these exciting opportunities found within text embeddings and generative AI. https://youtu.be/9SYpG_tFT38?si=BqFxnCisV2z5Xs2h&embedable=true New for technical practitioners: Intro to AI and ML Learn the big picture of how Google supports the data-to-AI journey and how generative AI is embedded in the AI development platform in our new course, Introduction to AI and Machine Learning on Google Cloud, designed mainly for technical practitioners — AI developers, data scientists, and ML engineers. The course covers the technologies, products and tools used to build an ML model, an ML pipeline, and a gen AI project based on the goals of data and AI practitioners. Made up of four primary modules that include labs and quizzes, this introductory course takes about six hours to complete and requires 12 credits. Stay on the cloud fast track with Innovators Plus Generative AI is  constantly evolving, and we’re developing new training materials to help you keep your career knowledge and skills up to date. An Innovators Plus subscription gives you access not only to generative AI content, but the full Google Cloud Skills Boost catalog of on-demand training for a whole year. You get $1,500 in developer benefits for only $299, including up to $1,000 in Google Cloud credits. Considering getting certified? You’ll also get an exam voucher, plus special access to Google Cloud experts and execs, live learning events, and more to round out your knowledge. Check out the details and sign up here. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here. Originally published here. Contributed by: Google Cloud Generative AI is a type of machine learning and artificial intelligence (AI) that is garnering a lot of attention right now, and we want to help keep you a step ahead by sharing the latest training available from Google Cloud. In this blog, we have compiled resources to help you learn about generative AI, how it can impact your business, and how to use it. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here . here We have brand-new generative AI training options, and are constantly adding to our training catalog on Google Cloud Skills Boost . This includes two learning paths that each feature comprehensive content: one is for the non-technical audience (introductory) ‘ Introduction to Generative AI learning path ’, and the other is for technical practitioners (more advanced) ‘ Generative AI for Developers learning path ’. Prefer less of a time commitment? Opt for individual courses and labs with the topics that suit your specific interest. brand-new generative AI training options, and are constantly adding to our training catalog on Google Cloud Skills Boost Introduction to Generative AI learning path Introduction to Generative AI learning path Generative AI for Developers learning path Generative AI for Developers learning path Powered by Google Cloud technology, generative AI is reimagining the way that organizations do business by leveraging data. The use cases are far-ranging , from having smarter conversations with and creating rich experiences for customers, to enhancing business processes, to helping developers write code, and more. use cases are far-ranging use cases are far-ranging Google Cloud has been working for decades to bring AI technology solutions to organizations, and our tools make it easier to build experiences across our cloud portfolio. Whether you’re executive-level, an IT decision maker, in a non-technical role, or a technical practitioner, we have videos, courses and labs to help you learn about the power of generative AI . Plus, labs give you direct access to Generative AI Studio and Vertex AI (Google Cloud’s machine learning platform) to get hands-on, technical experience. Get started with our training list below. Whether you’re executive-level, an IT decision maker, in a non-technical role, or a technical practitioner, we have videos, courses and labs to help you learn about the power of generative AI No-cost foundational courses: no technical experience needed Don’t have a lot of time but want to learn the basics? Our no-cost Introduction to Generative AI course is a great place to start. It takes about 45 minutes to finish. Our no-cost Introduction to Generative AI course is a great place to start. It takes about 45 minutes to finish. Introduction to Generative AI Introduction to Generative AI If getting started has you hungry to learn more, continue your no-cost learning to earn the Generative AI Fundamentals skill badge . It includes the course above, plus two other courses: ‘Introduction to Large Language Models’ and ‘Introduction to Responsible AI’. In total, this takes about two hours to complete and you’ll earn a digital skill badge to share on your resume and social channels to validate your foundational understanding of Google Cloud generative AI technology. continue your no-cost learning Generative AI Fundamentals skill badge Generative AI Fundamentals skill badge two hours to complete Dig in deeper with our generative AI learning paths For a more comprehensive learning experience, choose from our two new gen AI learning paths — a mix of courses, labs and skill badges. Please note: we plan to add new trainings to this list, so keep checking back for more. Introduction to Generative AI - this path includes no-cost video courses . Content is non-technical, introductory, and designed for the tech-adjacent audience: roles like sales, HR, marketing and operations. Here is a look at the content: Introduction to Generative AI Introduction to Generative AI Introduction to Generative AI this path includes no-cost video courses Introduction to Generative AI - course Introduction to Large Language Models - course Introduction to Responsible AI - course Generative AI Fundamentals - earn a skill badge Responsible AI: Applying AI Principals with Google Cloud - course Introduction to Generative AI - course Introduction to Large Language Models - course Introduction to Responsible AI - course Generative AI Fundamentals - earn a skill badge Responsible AI: Applying AI Principals with Google Cloud - course Generative AI for Developers - this path includes a combination of technical hands-on labs and courses and requires Google Cloud credits to complete. It is designed for technical developers who work with gen AI, and includes the introductory training as a prerequisite. Content includes: Generative AI for Developers Generative AI for Developers Generative AI for Developers Responsible AI for Developers - course Introduction to Image Generation - course Attention Mechanism - course Encoder-Decoder Architecture - course Transformer Models and BERT Model - course Create Image Captioning Models - course Introduction to Generative AI Studio - course Generative AI Explorer: Vertex AI - earn a skill badge Explore and Evaluate Models Using Model Garden - lab Prompt Design Using PaLM - lab Responsible AI for Developers - course Introduction to Image Generation - course Attention Mechanism - course Encoder-Decoder Architecture - course Transformer Models and BERT Model - course Create Image Captioning Models - course Introduction to Generative AI Studio - course Generative AI Explorer: Vertex AI - earn a skill badge Explore and Evaluate Models Using Model Garden - lab Prompt Design Using PaLM - lab Gain technical experience with labs We currently offer these generative AI labs for anyone looking to get some hands-on experience by working directly in Google Cloud: Get Started with Generative AI Studio (requires one credit) - introductory, 60 minutes Generative AI with Vertex AI: Getting Started (requires one credit) - introductory, 90 minutes Generative AI with Vertex AI: Prompt Design (requires one credit) - introductory, 90 minutes Vertex AI PaLM API: Qwik Start (requires one credit) - introductory, 60 minutes Integrate Search in Applications Using Gen AI App Builder (requires five credits) - introductory, 90 minutes Prompt Design using PaLM (requires one credit)  - intermediate, 90 minutes Explore and Evaluate Models using Model Garden (requires five credits) - intermediate, 60 minutes Get Started with Generative AI Studio (requires one credit) - introductory, 60 minutes Get Started with Generative AI Studio Get Started with Generative AI Studio Generative AI with Vertex AI: Getting Started (requires one credit) - introductory, 90 minutes Generative AI with Vertex AI: Getting Started Generative AI with Vertex AI: Getting Started Generative AI with Vertex AI: Prompt Design (requires one credit) - introductory, 90 minutes Generative AI with Vertex AI: Prompt Design Generative AI with Vertex AI: Prompt Design Vertex AI PaLM API: Qwik Start (requires one credit) - introductory, 60 minutes Vertex AI PaLM API: Qwik Start Vertex AI PaLM API: Qwik Start Integrate Search in Applications Using Gen AI App Builder (requires five credits) - introductory, 90 minutes Integrate Search in Applications Using Gen AI App Builder Integrate Search in Applications Using Gen AI App Builder Prompt Design using PaLM (requires one credit)  - intermediate, 90 minutes Prompt Design using PaLM Prompt Design using PaLM Explore and Evaluate Models using Model Garden (requires five credits) - intermediate, 60 minutes Explore and Evaluate Models using Model Garden Explore and Evaluate Models using Model Garden Text embeddings with DeepLearning.AI and Andrew Ng We have brand new content built in collaboration with DeepLearning.AI , taught by Andrew Ng, founder of DeepLearning.AI and a globally recognized leader on the topic. Enroll for no-cost in “Understanding and Applying Text Embeddings with Vertex AI” to learn various tasks like classification, outlier detection, text clustering, and semantic search. You’ll also learn to integrate them into LLM applications. Check out the video below to hear about these exciting opportunities found within text embeddings and generative AI. DeepLearning.AI DeepLearning.AI DeepLearning.AI DeepLearning.AI Enroll for no-cost in “Understanding and Applying Text Embeddings with Vertex AI” “Understanding and Applying Text Embeddings with Vertex AI” “Understanding and Applying Text Embeddings with Vertex AI” https://youtu.be/9SYpG_tFT38?si=BqFxnCisV2z5Xs2h&embedable=true https://youtu.be/9SYpG_tFT38?si=BqFxnCisV2z5Xs2h&embedable=true New for technical practitioners: Intro to AI and ML Learn the big picture of how Google supports the data-to-AI journey and how generative AI is embedded in the AI development platform in our new course, Introduction to AI and Machine Learning on Google Cloud , designed mainly for technical practitioners — AI developers, data scientists, and ML engineers. The course covers the technologies, products and tools used to build an ML model, an ML pipeline, and a gen AI project based on the goals of data and AI practitioners. Made up of four primary modules that include labs and quizzes, this introductory course takes about six hours to complete and requires 12 credits. Introduction to AI and Machine Learning on Google Cloud Introduction to AI and Machine Learning on Google Cloud this introductory course takes about six hours to complete and requires 12 credits. Stay on the cloud fast track with Innovators Plus Generative AI is  constantly evolving, and we’re developing new training materials to help you keep your career knowledge and skills up to date. An Innovators Plus subscription gives you access not only to generative AI content, but the full Google Cloud Skills Boost catalog of on-demand training for a whole year. You get $1,500 in developer benefits for only $299, including up to $1,000 in Google Cloud credits. Considering getting certified? You’ll also get an exam voucher, plus special access to Google Cloud experts and execs, live learning events, and more to round out your knowledge. Check out the details and sign up here . sign up here sign up here New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here. New Google Cloud customers from HackerNoon receive $300 plus an additional $50 in free credits to test, deploy, and explore Google Cloud for 90 days. Get started using the link here . here Originally published here . here Contributed by: Google Cloud

Learn Generative AI with Google Cloud: New Courses from Introductory to Advanced Level

The Transformer Algorithm with the Lowest Optimal Time Complexity Possible

সম্ভাব্য সর্বনিম্ন সর্বোত্তম সময়ের জটিলতা সহ ট্রান্সফরমার অ্যালগরিদম

Der Transformer-Algorithmus mit der geringstmöglichen optimalen Zeitkomplexität

El algoritmo de transformador con la menor complejidad de tiempo óptima posible

L'algorithme de transformateur avec la complexité temporelle optimale la plus faible possible

सबसे कम संभव इष्टतम समय जटिलता वाला ट्रांसफार्मर एल्गोरिदम

可能な限り最小の時間計算量を実現するトランスフォーマーアルゴリズム

가능한 가장 낮은 최적의 시간 복잡도를 갖는 변환기 알고리즘

O algoritmo do transformador com a menor complexidade de tempo ideal possível

Алгоритм трансформатора с минимально возможной оптимальной временной сложностью

Mümkün Olan En Düşük Optimal Zaman Karmaşıklığına Sahip Transformatör Algoritması

Thuật toán máy biến áp có độ phức tạp thời gian tối ưu thấp nhất có thể

具有最低最优时间复杂度的 Transformer 算法

Why 1-Bit Transformers Will Change the World

In 2015, a called OpenAI was formed to create “broadly and evenly distributed” AI. Fast forward to 2024, and OpenAI has transitioned into full-on for-profit mode, hoarding access to LLMs behind a transactional API service. Most recently, they’re seeking a . nonprofit $100 billion valuation The past decade of AI progress has been dominated by large tech companies like Google, Meta, and OpenAI releasing ever-larger proprietary language models. From Bard and Claude to GPT-4, much of the state-of-the-art in natural language processing (NLP) has remained concentrated in the hands of a few research labs. However, the long-term future of AI lies not in more private bigger models served exclusively through APIs but rather with open-source language models built in the open alongside communities. Open-Source Language Models In recent years, a handful of startups, universities, and dedicated individuals have helped pioneer this open model of language model development. The latest model continuing this open-source lineage is . Weighing in at 1.8 billion parameters, Danube demonstrates surprising capability even compared to other publicly available models many times its size. The team meticulously designed, trained, and validated Danube completely transparently, with the full report available on . H2O-Danube-1.8B H2O.ai arXiv Rather than hoarding access, released Danube's full parameters and training code openly on HuggingFace. Within days of the initial announcement, curious developers began freely experimenting with the model, showcasing rapid innovation generation simply not feasible with proprietary models. As of writing, the entire h2o-danube-1.8b-chat model has been download over 500 times on HuggingFace. H2O.ai Anyone can use the model with the library, following the below code, courtesy of h2o’s HuggingFace repo: transformers import torch
from transformers import pipeline

pipe = pipeline(
 "text-generation",
 model="h2oai/h2o-danube-1.8b-chat",
 torch_dtype=torch.bfloat16,
 device_map="auto",
)

# We use the HF Tokenizer chat template to format each message
# https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
 {"role": "user", "content": "Why is drinking water so healthy?"},
]
prompt = pipe.tokenizer.apply_chat_template(
 messages,
 tokenize=False,
 add_generation_prompt=True,
)
res = pipe(
 prompt,
 max_new_tokens=256,
)
print(res[0]["generated_text"])
# <|prompt|>Why is drinking water so healthy?</s><|answer|> Drinking water is healthy for several reasons: [...] H2O believes collaborating openly remains the ultimate key towards democratizing access to AI and unlocking benefits for the many rather than wealth for the few. Other Open-Source Language Models The open-source AI ecosystem continues expanding with developers globally collaborating on shared models. Beyond H2O-Danube-1.8B, numerous noteworthy initiatives aim to prevent concentration of knowledge within walled gardens. MPT Developed by startup MosaicML, the incorporates techniques like mixture-of-experts parallelization and context length extrapolation to improve efficiency. Machine Programming Transformer (MPT) Falcon Falcon’s biggest open-source LLM is a whopping beast, outperforming the likes of , , , and 180-billion parameter LLaMA-2 StableLM RedPajama . MPT At that size, it’s recommended to have 400 gigabytes of available memory to run the model. Mistral Founded by ex-Googlers and Meta researchers, Mistral released the 7 billion parameter in September 2022. Mistral 7B achieves competitive performance among open models nearly matching the closed GPT-3 in sample quality. Mistral 7B model Legacy Models Beyond newly launched models, earlier open-source models continue to empower developers. GPT2 from OpenAI and GPT-J from EleutherAI both hold historical significance despite lagging behind modern architectures. And Transformers like BERT gave rise to an entire subclass of NLP breakthroughs powering products globally. The democratization narrative only strengthens thanks to passionate communities generously contributing their creations back to common pools of knowledge. A More Equitable Future In many ways, proprietary language models risk recreating many inequities the tech industry continues wrestling with. Concentrating knowledge within wealthy organizations excludes smaller teams from shaping progress early on. And later makes integration prohibitively expensive once available purely through transactional APIs. Open-source models are vital to seeding a more equitable way forward. One where agency lies closer to diverse communities actually building concrete AI applications. The long arc of progress only Bends towards justice when people come together united behind the technology itself rather than any one organization seeking to control it. Danube and the open paradigm it represents offers but one glimpse into an alternate vision. One driven not by short-term profits or prestige but by empowering developers everywhere to freely build upon each other's shoulders. There will always remain space for proprietary work, but the true future of AI lies open. Community-Driven Innovation Releasing open-source models draws contributions from a motivated community of developers and researchers. This collaborative style of working in the open unlocks unique opportunities. Experts across organizations can peer review each other's work to validate techniques. Researchers can readily replicate and extend new ideas instead of reinventing the wheel. And software engineers can rapidly integrate and deploy innovations into customer offerings. Perhaps most promisingly, the open paradigm allows niche communities to gather around customizing models for specific use cases. Teams can sculpt versions tailored to particular topics like medicine, law, or finance which outperform generic models. These specialized models then get shared back to benefit the rest of the community. Together, groups make collective progress not possible within any single closed lab.

Why Open Source Language Models Are True “Open AI”

কেন ওপেন সোর্স ল্যাঙ্গুয়েজ মডেলগুলি সত্য "ওপেন এআই"

Warum Open-Source-Sprachmodelle echte „Open AI“ sind

Por qué los modelos de lenguaje de código abierto son una verdadera "IA abierta"

Pourquoi les modèles de langage Open Source sont de véritables « IA ouvertes »

क्यों ओपन सोर्स लैंग्वेज मॉडल सच्चे "ओपन एआई" हैं

オープンソース言語モデルが真の「オープン AI」である理由

오픈 소스 언어 모델이 진정한 "Open AI"인 이유

Por que os modelos de linguagem de código aberto são verdadeiras “IA aberta”

Почему языковые модели с открытым исходным кодом являются истиной «Открытый ИИ»

Açık Kaynak Dil Modelleri Neden Doğrudur?

Tại sao các mô hình ngôn ngữ nguồn mở lại là “AI mở” đích thực

为什么开源语言模型是真正的“开放人工智能”

320 Stories To Learn About Deep Learning

Revolutionizing Creative Text Generation with Quality-Diversity through AI Feedback

Let's learn about via these 59 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Tensorflow /Learn Repo 1. Quantum Machine Learning Using TensorFlow Quantum INTRODUCTION 2. What Does Facebook Hydra Mean For The Future of Python Ever since its inception in the year 1089 by Guido Van Rossum, the programming language Python has along far away. Sheldon did its creator knew that Python would in today's world be utilized for a variety of purposes such as research, development, scripting, among many others. Built as a successor in the ABC language, Python does not just find its applications in software development but also in research. 3. YouTube's Recommendation Engine: Explained Every successful tech product, by the very definition, is a result of some technological marvels working with impeccable user experience to solve a key problem for the users. One such marvel is the recommendation engine by YouTube. 4. NSFW Filter Introduction: Building a Safer Internet Using AI Filtering out NSFW images with a web extension built using TensorFlow JS. 5. Why ML in Production is (still) Broken and Ways we Can Fix it Machine Learning, Deep Learning development in production was still broken. ZenML, an extensible, open-source MLOps framework for production-ready ML pipelines. 6. Deploy First TensorFlow Model in Android App Simple linear regression is useful for finding the relationship between two continuous variables. One is a predictor or independent variable and the other is a response or dependent variable. It looks for a statistical relationship but not a deterministic relationship. Relationship between two variables is said to be deterministic if one variable can be accurately expressed by the other. For example, using temperature in degrees Celsius it is possible to accurately predict Fahrenheit. 7. How to Run Machine-Learning Models in the Browser using ONNX Learn how to use ONNX Runtime Web to deploy machine-learning models natively to the browser. 8. Training Your Models on Cloud TPUs in 4 Easy Steps on Google Colab You have a plain old TensorFlow model that’s too computationally expensive to train on your standard-issue work laptop. I get it. I’ve been there too, and if I’m being honest, seeing my laptop crash twice in a row after trying to train a model on it is painful to watch. 9. 10 Must-Try Open Source Tools for Machine Learning Machine learning is the future. But will machines ever extinct humans? 10. Efficient Implementation of MobileNet and YOLO Object Detection Algorithms for Image Annotation The objective of the problem is to implement classification and localization algorithms to achieve high object classification and labelling accuracies, and train models readily with as least data and time as possible. The solution to the problem is considered in the following blog. 11. Cocktail Alchemy: Creating New Recipes With Transformers Build a transformer model with natural language processing to create new cocktail recipes from a cocktail database. 12. Auto-Generating Lyrics With TensorFlow and Machine Learning: A How-To Guide Creating a bot that, given a starting phrase, would generate its own lyrics, powered by a machine learning model that would have learned from existing songs. 13. How I Transfer an Artistic Style to Any Image Machine learning and artificial intelligence have been on my radar for years now, but more as a concept and “thing I should know about.” I didn’t feel that I had the free time or skills to dig into it. However, my attitude about machine learning has changed in the past few months. I have seen new and easier tools become accessible to the public. In this post I will walk you through how to transfer an art style to any image using some of these tools. 14. Style Transferring with TensorFlow Style transfer is a computer vision-based technique combined with image processing. Learn about style transfer with Tensorflow, a prominent framework in AI & ML 15. Time Series Forecasting with TensorFlow.js Pull stock prices from online API and perform predictions using Recurrent Neural Network & Long Short Term Memory (LSTM) with TensorFlow.js framework 16. Manipulación de tensores en PyTorch. ¡El primer paso para el deep learning! *Nota: Contactar a Omar Espejel (omar@tsc.ai) para cualquier observación. Cualquier error es responsabilidad del autor. 17. TLDR Newsletter Week of August 5th Highlights 18. Flax: Google’s Open Source Approach To Flexibility In Machine Learning Thinking of Machine Learning, the first frameworks that come to mind are Tensorflow and PyTorch, which are currently the state-of-the-art frameworks if you want to work with Deep Neural Networks. Technology is changing rapidly and more flexibility is needed, so Google researchers are developing a new high performance framework for the open source community: Flax. 19. Top 3 Face Datasets and How to Work with Them An image dataset contains specially selected digital images intended to help train, test, and evaluate an artificial intelligence (AI) or machine learning (ML) 20. PyTorch vs TensorFlow: Who has More Pre-trained Deep Learning Models? Given the importance of pre-trained Deep Learning models, which Deep Learning framework - PyTorch or TensorFlow - has more of these models available to users is 21. Rare Datasets for Computer Vision Every Machine Learning Expert Must Work With Have you ever being in a situation to guess another person’s age? Well May be YES!! How about playing games like finding things in minimum time? or about finding the written character where your doctor wrote in the prescription when you are sick? 22. How I Built an AI to Detect License Plate Number Registration (ANPR) This Car Mod Is A Privacy Nightmare! (AI Number Plate Reader with Python, Tensorflow, OpenCV, OpenALPR) 23. How to Fine Tune a 🤗 (Hugging Face) Transformer Model How to fine-tune a Hugging Face Transformer model for Sequence Classification 24. Top 10 Data Science Libraries in Python Data Science Libraries that will shine this year. 25. What Neural Networks Teach Us About Schizophrenia Pretrained Artificial Neural Networks used to work like a Blackbox: You hand them an input and they predict an output with a certain probability — but without us knowing the internal processes of how they came up with their prediction. A Neural Network to recognize images usually consists of around 20 neuron layers, trained with millions of images to tweak the network parameters to give high quality classifications. 26. Classify Handwritten Digits using Deep learning with Tensorflow Deep learning is a subpart of machine learning and artificial intelligence which is also known as deep neural network this networks capable of learning unsupervised from provided data which is unorganized or unlabeled. today, we will implement a neural network in 6 easy steps using TensorFlow  to classify handwritten digits. 27. Artificial Intelligence Vs Machine Learning: What's the difference? AI and Machine Learning are predominant terms that are creating a lot of buzz in the technology world. The terms can often be used interchangeably but that’s not the case, AI and ML are way more different from each other in their approach, algorithms and logical thinking. 28. How To Build Links Detector That Making Links in Your Book Clickable How I built a link detector for your smart phone to browse links printed in books. 29. Computer Vision Could Improve Health and Workplace Safety Recent developments in the field of training Neural Networks (Deep Learning) and advanced algorithm training platforms like Google’s TensorFlow and hardware accelerators from Intel (OpenVino), Nvidia (TensorRT) etc., have empowered developers to train and optimize complex Neural Networks in small edge devices like Smart Phones or Single Board Computers. 30. From TF to TFLite: Deploying ML Models on Mobile [Part 2] This is part 2 of the two-part article on deploying ML models on mobile. We saw how to convert our ML models to TfLite format here. For those of you who came here first, I recommend you click on the above link to get the whole picture.  If you just want the android part ,the demo app we are building has a GAN model generating handwritten digits and a classifier model predicting the generated digit. 31. From TF to TFLite: Deploying ML Models on Mobile [Part 1] tl;dr - Link to code: TensorFlow GAN model. So the other day I was talking to my rubber ducky about how G-Board predicts my next word, even when those words are entirely made up by me, in that how it actually learns on-device. How amazingly Netflix, Amazon, Google Maps make use of machine learning in their apps. How does machine learning on apps even work? Does the model learn even after being deployed? Can I deploy a GAN model on mobile? 32. How to Use TensorFlow in Python: Google‘s Open-Source Library For Deep Learning You might not always know it, but Deep Learning is everywhere. We explain how to use TensorFlow, Google's Library For Deep Learning, in Python. 33. Build Your Own Voice Recognition Model with Tensorflow While I'm usually a JavaScript person, there are plenty of things that Python makes easier to do. Doing voice recognition with machine learning is one of those. 34. C++ to WebAssembly using Bazel and Emscripten How to get Bazel and Emscripten to compile C++ to WebAssembly or JavaScript 35. Approach Pre-Trained Deep Learning Models With Caution Pre-trained models are easy to use, but are you glossing over details that could impact your model performance? 36. Tensorflow Vs. Keras: Comparison by building a model for image classification Yes, as the title says, it has been very usual talk among data-scientists (even you!) where a few say, TensorFlow is better and some say Keras is way good! Let’s see how this thing actually works out in practice in the case of image classification. 37. Human Detection System Using RaspberryPi, Thermal Camera and Machine Learning Triggering reliable events based on the presence of people has been the dream of many geeks and DIY automators for a while. Having your house to turn the lights on or off when you enter or exit your living room is an interesting application, for instance. Most of the solutions out there to solve these kinds of problems, even more high-end solutions like the Philips Hue sensors, detect motion, not actual people presence — which means that the lights will switch off once you lay on your couch like a sloth. 38. How Blockchain & AI Integration is Changing Business Landscape? The potential of Blockchain is no lesser than Artificial intelligence. If you have taken a look at them, you must already know the impacts of these technologies on various industries. 39. Lessons for Improving Training Performance — Part 1 Part 1: Lower precision & larger batch size are standard now 40. [Hacking Tinder] Train an AI to Auto-Swipe for You 🖖 Auto-tinder was created to train an AI using Tensorflow and Python3 that learns your interests in the other sex and automatically plays the tinder swiping-game for you. 41. Fabio Manganiello on Home-Made Computer Vision, IoT, Automation, AI Fabio Manganiello writes about solutions he's discovered while building a platform, library of plugins and an API to connect/manage any device and service through any backend, allowing users to easily set up any kind of automation. Fabio is based in Amsterdam, the Netherlands, and has been nominated for a 2020 #Noonie for exceptional contributions to the IoT tag category on Hacker Noon. 42. Efficient Model Training in the Cloud with Kubernetes, TensorFlow, and Alluxio Open Source This article presents the collaboration of Alibaba, Alluxio, and Nanjing University in tackling the problem of Deep Learning model training in the cloud. Various performance bottlenecks are analyzed with detailed optimizations of each component in the architecture. This content was previously published on Alluxio's Engineering Blog, featuring Alibaba Cloud Container Service Team's case study (White Paper here). Our goal was to reduce the cost and complexity of data access for Deep Learning training in a hybrid environment, which resulted in over 40% reduction in training time and cost. 43. The Full Story behind Convolutional Neural Networks and the Math Behind it Convolutional Neural Networks became really popular after 2010 because they outperformed any other network architecture on visual data, but the concept behind CNN is not new. In fact, it is very much inspired by the human visual system. In this article, I aim to explain in very details how researchers came up with the idea of CNN, how they are structured, how the math behind them works and what techniques are applied to improve their performance. [44. Differential Privacy with Tensorflow 2.0 :  Multi class Text Classification Privacy](https://hackernoon.com/differential-privacy-with-tensorflow-20-multi-class-text-classification-privacy-yk7a37uh) Introduction 45. What Is Edge AI? Edge AI—also referred to as on-device AI—commonly refers to the components required to run an AI algorithm locally on a hardware device. 46. Mueller Report for Nerds! Spark meets NLP with TensorFlow and BERT (Part 1) Photo by Michael on Unsplash 47. [Tutorial] Build a Gender Classifier for Live Webcam Stream using Tensorflow and OpenCV Training a Neural Network from scratch suffers two main problems. First, a very large, classified input dataset is needed so that the Neural Network can learn the different features it needs for the classification. 48. Objects Classification Using CNN-based Model — All the images (plots) are generated and modified by the Author. 49. Can I Grade Loans Better Than LendingClub? In case you missed it, I built a neural network to predict loan risk using a public dataset from LendingClub. Then I built a public API to serve the model’s predictions. That’s nice and all, but… how good is my model? 50. C++ to WebAssembly using Bazel and Emscripten How to get Bazel and Emscripten to compile C++ to WebAssembly or JavaScript 51. Training Your Own Text Classification Model From Scratch With Tensorflow Is As Easy As ABC Hello ML Newb! In this article, you will learn to train your own text classification model from scratch using Tensorflow in just a few lines of code. 52. RethNet Model: Object-by-Object Learning for Detecting Facial Skin Problems In August 2019, a group of researchers from lululab Inc propose the state-of-the-art concept using a semantic segmentation method to detect the most common facial skin problems accurately. The work is accepted to ICCV 2019 Workshop. 53. Uber AI Labs Senior Research Scientist Talks TensorFlow 2.0 [Interview] There’s no doubt that TensorFlow is one of the most popular machine learning libraries right now. However, newbie developers who want to experiment with TensorFlow often face difficulties in learning TensorFlow; the framework has a not unjustified reputation for having a steep learning curve that can make it hard for developers to get to grips with quickly. 54. Training Machine Learning Models Using TensorFlow or PyTorch I will show you how gradient descent works, which is in the deepest deep of machine learning. 55. Building Machine Learning Models With TensorFlow In this article, I will share with you some useful tips and guidelines that you can use to better build better deep learning models. 56. Build A Smart Baby Monitor Using a RaspberryPi and Tensorflow Some of you may have noticed that it’s been a while since my last article, despite winning this year's IoT Noonies award (btw thanks to all of you who voted, that means a lot to me!). 57. #Switch2Swift for Deep Learning If you are interested, what the recent fast.ai advanced and closed Deep Learning Class had to say about Google’s Swift for Tensorflow project, you might find this post interesting. Even if you attended the class, you should find here hopefully a good overview (with links into the class, presentations, and additional material), what Swift for Tensorflow is and why it might be relevant. 58. Scale Vision Transformers (ViT) Beyond Hugging Face Speed up state-of-the-art ViT models in Hugging Face 🤗 up to 2300% (25x times faster ) with Databricks, Nvidia, and Spark NLP 🚀 59. Loan Risk Prediction Using Neural Networks A Step-by-Step Guide (With a Healthy Dose of Data Cleaning) Thank you for checking out the 59 most read stories about Tensorflow on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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