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I'm sharing this link with my network once โ and only the first 5 people who enroll through it lock in a deal that has never been offered before.
๐ Lifetime access to HelloEncyclo โ every AI, ML & Data Science course ever built โ for ~$41. Once. Forever.
This isn't a drill. This isn't a rerun.
This is the founding-member price โ and it disappears the moment the first 250 seats globally are gone.
โ 13 courses live right now
โ 40+ more in 2โ3 weeks
โ Every future course included automatically
โ 15-day money-back โ full refund, no questions
Code: PRESALE-BOOK-WAVE-2GFG
(Log in with Gmail ยท valid once ยท applies at checkout)
๐ First 5. That's it.
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A new collection of free courses has been added:
๐ https://github.com/dair-ai/ML-Course-Notes
Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. ๐
Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. ๐ง
What's inside:
โข Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
โข A table with lectures, descriptions, videos, notes, and authors
โข Links to the original lectures and accompanying notes
โข WIP markers for incomplete materials
โข Instructions for contributors on adding and improving notes
The idea was appreciated. ๐
Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. ๐บ๏ธ
#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource
โจ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
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๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
๐ https://github.com/dair-ai/ML-Course-Notes
Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. ๐
Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. ๐ง
What's inside:
โข Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
โข A table with lectures, descriptions, videos, notes, and authors
โข Links to the original lectures and accompanying notes
โข WIP markers for incomplete materials
โข Instructions for contributors on adding and improving notes
The idea was appreciated. ๐
Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. ๐บ๏ธ
#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource
โจ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
โญ๏ธ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
GitHub
GitHub - dair-ai/ML-Course-Notes: ๐ Sharing machine learning course / lecture notes.
๐ Sharing machine learning course / lecture notes. - dair-ai/ML-Course-Notes
โค8
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Join our livestream with Marina Wyss, Senior Applied Scientist at Twitch, as we discuss how to break into AI Engineering in 2026.
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Many people interested in AI Engineering are asking the same questions:
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Why should you join?
Many people interested in AI Engineering are asking the same questions:
โ Where do I start?
๐ค Do I need deep math first?
๐ง Should I focus on ML, LLMs, RAG, or AI agents?
๐งญ How do I avoid wasting time learning the wrong things?
๐ How do I go from learning to becoming hireable?
If youโre interested in AI Engineering but unsure how to approach it, this livestream is for you.
What youโll learn
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โฆ Where beginners should start
โฆ What skills and topics actually matter
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โฆ How to think about becoming hireable in AI
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Sign up for FREE and save your seat: luma.com/qgz4g4r7
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5 Fun Papers That Explain LLMs Clearly ๐โจ
Want to understand LLMs better? Start with these five foundational papers that explain how they work. ๐ค
Large language models (LLMs) can feel complicated at first. There are transformers, attention layers, scaling laws, pretraining, instruction tuning, human feedback, retrieval, and many other ideas around them. ๐ง But the best way to understand large language models is not to start with a huge textbook. A better way is to read a few important papers that each explain one major part of the system. ๐ This article is part of a fun series where we learn by exploring core ideas, practical projects, and the research papers behind modern technology. ๐ฌ In this article, we will go through five papers that explain how LLMs work. So, let's get started. ๐
More: https://www.kdnuggets.com/5-fun-papers-that-explain-llms-clearly
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Want to understand LLMs better? Start with these five foundational papers that explain how they work. ๐ค
Large language models (LLMs) can feel complicated at first. There are transformers, attention layers, scaling laws, pretraining, instruction tuning, human feedback, retrieval, and many other ideas around them. ๐ง But the best way to understand large language models is not to start with a huge textbook. A better way is to read a few important papers that each explain one major part of the system. ๐ This article is part of a fun series where we learn by exploring core ideas, practical projects, and the research papers behind modern technology. ๐ฌ In this article, we will go through five papers that explain how LLMs work. So, let's get started. ๐
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GRU โ remembers efficiently
GAN โ creates realistic content
RNN โ understands sequences
LSTM โ remembers long context
https://t.me/CodeProgrammer
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Forwarded from Machine Learning
If you already have 200 open tabs with courses, articles, and GitHub repositories on ML, this repository might save the situation a bit. ๐
Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. ๐ค
Instead of endless Google searches, everything is organized into categories:
โข fundamentals of machine learning
โข neural networks and modern architectures
โข tasks and application areas
โข datasets
โข libraries and tools
โข fairness and AI ethics
โข production ML and MLOps
Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. ๐
I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. โ ๏ธ
https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
#MachineLearning #DeepLearning #AI #MLOps #DataScience #TechResources
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๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
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๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. ๐ค
Instead of endless Google searches, everything is organized into categories:
โข fundamentals of machine learning
โข neural networks and modern architectures
โข tasks and application areas
โข datasets
โข libraries and tools
โข fairness and AI ethics
โข production ML and MLOps
Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. ๐
I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. โ ๏ธ
https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
#MachineLearning #DeepLearning #AI #MLOps #DataScience #TechResources
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Forwarded from Vinayak Chiluka
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Don't want to read about AI Engineering, but actually want to build something? ๐ ๏ธ
We've released a collection of 50+ practical tutorials on AI Engineering as open source. ๐
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๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
We've released a collection of 50+ practical tutorials on AI Engineering as open source. ๐
Inside, you'll find step-by-step projects on:
โข AI agents and multi-agent systems ๐ค
โข RAG (Agentic, Vision, and Local) ๐
โข MCP agents โ๏ธ
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GitHub
GitHub - Sumanth077/Hands-On-AI-Engineering: A curated collection of practical AI projects implementing OCR systems, RAG, AI agentsโฆ
A curated collection of practical AI projects implementing OCR systems, RAG, AI agents, and other AI use cases. - Sumanth077/Hands-On-AI-Engineering
โค4๐2
Agentic_Design_Patterns.pdf
19.2 MB
Agentic Design Patterns - a free 421-page document from a senior Google engineer. ๐โจ
It's rare to find materials of such a large volume where the author doesn't try to sell a course after every chapter. ๐ก
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It's rare to find materials of such a large volume where the author doesn't try to sell a course after every chapter. ๐ก
Inside:
โฌฉ agent architectures
โฌฉ multi-agent systems
โฌฉ memory and context management
โฌฉ orchestration and task planning
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Kaggle and Google are launching a free 5-day intensive course on VibeCoding. ๐
Over five days, they will explain how agent-based coding works, how to configure skills, memory, and context, and how to make AI assistants not only useful but also safe. ๐ค The program includes daily tasks, live streams, discussions, and practical projects. ๐ป
After completion, participants will receive an official certificate that can be added to their portfolio. ๐
Start date: June 15th. ๐
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If you've long wanted to properly understand agent-based coding, this is a great opportunity to dive in. ๐
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Start date: June 15th. ๐
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โ๏ธ Python Tutorial โ a detailed course for beginners!
A comprehensive Python training course that systematically covers the basics of the language: variables, conditions, loops, functions, lists, dictionaries, OOP, etc. The material is accompanied by code examples and clear explanations, making it suitable even for those who are just starting their journey in programming.
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A comprehensive Python training course that systematically covers the basics of the language: variables, conditions, loops, functions, lists, dictionaries, OOP, etc. The material is accompanied by code examples and clear explanations, making it suitable even for those who are just starting their journey in programming.
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#Python #Tutorial #Coding #Programming #LearnToCode #TechSkills
โจ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
โญ๏ธ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO