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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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16 GB RAM. No cloud subscription. Which local AI model actually fits?
How AI Helps built a free Telegram model picker. Choose your task, RAM or VRAM, language, runtime, and commercial-use requirement.
Then compare a shortlist by memory, license, sources, download options, and launch commands when available.
Join How AI Helps and open the pinned model-picker guide
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Maths, CS & AI Compendium: A free textbook for aspiring AI/ML engineers
🚀 A large open-source compendium on mathematics, computer science, and AI has gone viral on GitHub. The project already has around 6.3K stars.
📚 The author positions it as a "non-traditional textbook" for practitioners: less dry notation, more intuition, connections between topics, and real-world context.
📖 It contains 20 chapters:
* Vectors, matrices, calculus
* Statistics and probability
* Machine learning and deep learning
* NLP, computer vision, audio/speech
* Multimodal learning and autonomous systems
* GNN, OS, algorithms
* Production engineering, GPU/SIMD
* AI inference, ML systems design, and applied AI
🤖 There is also a MCP server so that Claude Code, Cursor, VS Code, and other AI assistants can use the compendium as a local knowledge base.
💡 This is a great resource for those who want to not just "learn ML," but to build a solid foundation: mathematics → CS → ML systems → modern AI.
🔗 GitHub: https://github.com/HenryNdubuaku/maths-cs-ai-compendium
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🔖 Comprehensive Practical Course on Reinforcement Learning
We've found a repository that will help you learn Reinforcement Learning, from basic concepts to advanced algorithms.
The author supports the theory with practical examples using TensorFlow, making the material ideal for self-study.
⛓️ Link to GitHub
https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow
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Reinforcement Learning Methods and Tutorials 🧠📚
In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years.
Learning Resources: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow 🚀
Here's a collection of simple materials on methods and practical guides, covering both basic reinforcement learning algorithms and modern, recently developed, and updated advanced algorithms. 📖✨
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🔖 One of the most useful books on Agentic AI
This is not just a textbook, but a comprehensive overview of modern LLMs, model training, RL, inference, quality assessment, and building AI agents.
It's an excellent option to get a holistic picture and understand which topics deserve deeper study.
⛓️ Link to the book
https://arxiv.org/abs/2606.24937
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Transformers become more understandable when you can "poke" the model directly. 🧠✨
Transformer Explainer is an interactive visualization tool for studying how text-generating transformer-based models, such as GPT, work. 🔍
It helps connect the architecture with real behavior by running a live GPT-2 directly in the browser, allowing you to enter your own text and showing how the internal components work together to predict the next tokens. 🔄📝
Key features: 🌟
- Live GPT-2 in the browser - experiment without setting up a separate model server 💻
- Your own text - try your own prompts and see how the model processes them ✍️
- Internal components - observe the operations working inside the transformer 🔧
- Focus on predicting the next token - link each visual step to the model's predictions 🎯
- Local development - clone the repository, install dependencies, and run via npm for in-depth study ⚙️
It's open-source (MIT license). 📜
https://github.com/poloclub/transformer-explainer
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Python Programming for Beginners: Learn Python from Scratch
Python Programming for Beginners: Learn Python from Scratch (Master Data Analysis, Step-by-Step with Practice Exercises)…
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Stop searching for generative AI resources one category at a time. 🚀
Awesome Generative AI is a curated directory of generative AI projects and services for builders exploring the ecosystem. 🌐
It helps you compare where to look next by organizing links and short descriptions across models, tools, agents, media, and learning resources. 📊
Key features:
• Text stack – browse models, chatbots, search engines, writing tools, research tools, and leaderboards ✍️
• Coding toolkit – find coding assistants, developer tools, playgrounds, and local LLM deployment options 💻
• Agent directory – scan autonomous agent projects and custom assistant resources 🤖
• Multimodal map – explore image, video, audio, and music tools in dedicated sections 🎨🎵
• Learning library – use recommended reading, milestones, courses, guides, and related lists to build context 📚
It’s open-source (CC0-1.0 license). 🔓
Repo: https://github.com/steven2358/awesome-generative-ai
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Hugging Face Viewer is now at 2300 viewable models! 😊 Would love more feedback and ideas!
It's a free interactive graph visualizer for learning about the architectures of open source AI models! 🚀
Hovering nodes in the graph links to a definitions + animation and the paper that introduced it!
🌟 hfviewer.com
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Diving deep into Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP. 🤖🧠
Lectures: 🎓📚
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🚀 Looking for a portfolio-ready NLP project?
I recently published an end-to-end walkthrough on Towards Data Science using Kaggle’s Spooky Author Identification dataset.
You’ll see how far classical NLP can go with:
📝 Bag-of-Words and TF-IDF
🔤 Character n-grams
📊 Model comparison
🧩 Ensemble stacking
It’s a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step:
🔗 https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/
🎓 Free Courses on Claude and AI From Anthropic
Anthropic has expanded its educational platform, Anthropic Academy. It now offers more than 20 free courses on using Claude and AI-powered tools.
No programming experience is required. There are programs for students, educators, nonprofits, and small businesses. The courses cover how to give models clear instructions, evaluate their responses, and automate everyday tasks.
Developers can also take dedicated courses on the Claude API, Claude Code, Model Context Protocol, Agent Skills, and cloud integrations.
All courses are free, and you don't need a Claude account to enroll. All materials and videos are in English.
📌 Participants receive official certificates after completing many of the programs.
➡️ Find all the courses here.
https://anthropic.com/learn/courses
Planning to take one?
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A Chinese developer has released an open-source replacement for NumPy that performs calculations on GPUs. It's called CuPy 🚀. In many cases, it's enough to replace a single line:
import cupy as cp