Machine Learning with Python
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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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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. πŸ”

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. πŸ”„πŸ“

Key features: 🌟

- Live GPT-2 in the browser - experiment without setting up a separate model server πŸ’»
- Your own text - try your own prompts and see how the model processes them ✍️
- Internal components - observe the operations working inside the transformer πŸ”§
- 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 βš™οΈ

It's open-source (MIT license). πŸ“œ

https://github.com/poloclub/transformer-explainer

#AI #MachineLearning #GPT #DataScience #TechTools #OpenSource

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My new hobby: searching for beautiful projects on GitHub and being inspired by their design. πŸš€βœ¨

https://www.gitsearchai.com/ πŸ”—

#GitHub #OpenSource #Tech #Programming #Design #Inspiration

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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! πŸš€

Hovering nodes in the graph links to a definitions + animation and the paper that introduced it!

🌟 hfviewer.com

#HuggingFace #AI #MachineLearning #OpenSource #TechNews #DataViz

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Forwarded from Machine Learning
Maths, CS & AI Compendium: A free textbook for aspiring AI/ML engineers

πŸš€ A large open-source compendium on mathematics, computer science, and AI has gone viral on GitHub. The project already has around 6.3K stars.

πŸ“š The author positions it as a "non-traditional textbook" for practitioners: less dry notation, more intuition, connections between topics, and real-world context.

πŸ“– 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

πŸ€– 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.

πŸ’‘ This is a great resource for those who want to not just "learn ML," but to build a solid foundation: mathematics β†’ CS β†’ ML systems β†’ modern AI.

πŸ”— GitHub: https://github.com/HenryNdubuaku/maths-cs-ai-compendium

#AI #MachineLearning #ComputerScience #Maths #OpenSource #DevCommunity

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Stop searching for generative AI resources one category at a time. πŸš€

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. πŸ“Š

Key features:
β€’ Text stack – browse models, chatbots, search engines, writing tools, research tools, and leaderboards ✍️
β€’ Coding toolkit – find coding assistants, developer tools, playgrounds, and local LLM deployment options πŸ’»
β€’ Agent directory – scan autonomous agent projects and custom assistant resources πŸ€–
β€’ Multimodal map – explore image, video, audio, and music tools in dedicated sections 🎨🎡
β€’ Learning library – use recommended reading, milestones, courses, guides, and related lists to build context πŸ“š

It’s open-source (CC0-1.0 license). πŸ”“

Repo: https://github.com/steven2358/awesome-generative-ai

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