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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
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Agentic Design Patterns - a free 421-page document from a senior Google engineer. 📄✨
It's rare to find materials of such a large volume where the author doesn't try to sell a course after every chapter. 💡
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If you already have 200 open tabs with courses, articles, and GitHub repositories on ML, this repository might save the situation a bit. 😅
Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. 🤖
Instead of endless Google searches, everything is organized into categories:
• fundamentals of machine learning
• neural networks and modern architectures
• tasks and application areas
• datasets
• libraries and tools
• fairness and AI ethics
• production ML and MLOps
Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. 📝
I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. ⚠️
https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
#MachineLearning #DeepLearning #AI #MLOps #DataScience #TechResources
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5 Fun Papers That Explain LLMs Clearly 📚✨
Want to understand LLMs better? Start with these five foundational papers that explain how they work. 🤖
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. 🧠 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. 📄 This article is part of a fun series where we learn by exploring core ideas, practical projects, and the research papers behind modern technology. 🔬 In this article, we will go through five papers that explain how LLMs work. So, let's get started. 🚀
More: https://www.kdnuggets.com/5-fun-papers-that-explain-llms-clearly
#LLM #AI #MachineLearning #DeepLearning #DataScience #Tech
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A new collection of free courses has been added:
🔗 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. 📚
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. 🧠
What's inside:
• Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
• A table with lectures, descriptions, videos, notes, and authors
• Links to the original lectures and accompanying notes
• WIP markers for incomplete materials
• Instructions for contributors on adding and improving notes
The idea was appreciated. 👍
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. 🗺️
#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource
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🚀 Demystifying Activation Functions! 🧠✨
Ever wondered why activation functions are so critical in neural networks? 🤔🤖
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Most people don’t fail because they lack ambition 🎯
They fail because their time and energy leak into distractions before they ever reach the goal 🕰💔
This visual explains it perfectly: 📊
Time + Energy are only useful when filtered through discipline 🧠🛡
Without discipline, distractions absorb everything: 🌊
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• poor sleep 😴
• stress-driven habits 🤯
• multitasking disguised as productivity 🔄
And in high-performance environments, this becomes a leadership issue, not just a personal one 🏢📉
I see this often in corporate wellness workshops and executive coaching sessions. 🗣
Leaders want better focus, resilience, and performance ⚡️
But the real challenge is not motivation 🚫🔥
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Discipline is not punishment ⛔️
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Sometimes that means: ✔️
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Because peak performance is rarely about doing more 🏃
It’s about allowing less distraction to consume what matters most 🎯✨
Your goals are not only built by effort 💪
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14 minutes with an Anthropic engineer will teach you more about building agents 🤖 than most devs figure out in months of trial and error 🛠.
Same guy who wrote “Building Effective Agents”, the post every AI builder has bookmarked 📑.
No fluff. No 47-tool frameworks. Just the patterns that actually work in production 🚀:
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If you’re shipping AI products in 2026 and haven’t watched this, you’re doing it on hard mode 🎮.
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☕️ Python Tutorial — 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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Kaggle and Google are launching a free 5-day intensive course on VibeCoding. 🚀
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Don't want to read about AI Engineering, but actually want to build something? 🛠️
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GRU → remembers efficiently
GAN → creates realistic content
RNN → understands sequences
LSTM → remembers long context
Introduction to Critical Infrastructure Protection (ICIP) 🛡️
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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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If you’re interested in AI Engineering but unsure how to approach it, this livestream is for you.
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Data Science Interview Questions
💡 Here is your curated list for Data Science interviews!
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#DataScience #AI #MachineLearning #LLM #TechJobs #InterviewPrep
🔖 The Big Book on Fine-Tuning LLMs
A free 115-page book dedicated to the retraining of large language models. 📚
It's suitable for those who want to understand how to prepare datasets, configure training, and improve the quality of LLMs for their tasks. 🚀
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The only LLM cheat sheet you'll ever need 🚀
Covers the main concepts, architectures, and practical applications.
### Basics
- Tokens (tokenization, BPE)
- Embeddings (cosine similarity)
- Attention mechanism (Attention formula, Multi-Head Attention)
### Transformer architecture and its variants
- BERT (models with only an encoder)
- GPT (models with only a decoder)
- T5 (models with an encoder and a decoder)
### Large language models (LLMs)
- Prompting (context length, Chain-of-Thought)
- Pre-training (SFT, PEFT/LoRA)
- Preference tuning (Reward Model, Reinforcement Learning)
- Optimizations (Mixture of Experts, Distillation, Quantization)
### Applications
- LLM-as-a-Judge (LaaJ)
- RAG (Retrieval-Augmented Generation)
- Agents (ReAct)
- Reasoning models (Scaling)
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reader3 📚✨
When you want to connect an AI like Gemini to help you analyze books or content, copying text from a reader usually becomes a hassle. 😩💻
Especially if you want to discuss a book by chapters. Highlighting text manually and copying it disrupts the flow and feels like a waste of time. ⏳🚫
Yesterday, Andrzej Karpati, a well-known AI expert, released a new project to the public: reader3, which solves this problem very neatly. 🎉🛠️ It's a lightweight EPUB reader that allows you to read a book together with AI. 🤖📖
Its interface is as minimalist as possible: only the necessary reading and navigation functions. 📉🧭 You can also manage your library through folders. 📁✨
The key feature is that it breaks an EPUB into chapters and displays the content one chapter at a time. 🔓📄
This makes it easy to copy the needed part of the book and pass it to a large model for analysis or discussion. 📋🔄 It significantly improves the reading experience when paired with AI. 🚀🧠
And it's very easy to get started - just run two commands via uv. ⚡🛠️ As a result, it's an excellent tool for those who love reading and want to use AI as a companion for text analysis. 📚🤝🤖
📁 Language: #Python 61.0%
⭐️ Stars: 1.5k
➡️ Link to GitHub https://github.com/karpathy/reader3
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🔖 Interactive textbook on probability theory and statistics 📊✨
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