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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Machine Learning with Python pinned Β«Create your own AI assistant for free in 5 minutes. It's a familiar problem: everyone wants a personal AI assistant, but building one from scratch usually means servers, API keys, integrations, maintenance, and a ton of technical overhead. Amplify takes…»
Forwarded from Machine Learning
Diving deep into Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP. πŸ€–πŸ§ 

Lectures: πŸŽ“πŸ“š
https://github.com/kmario23/deep-learning-drizzle

#DeepLearning #MachineLearning #AI #ReinforcementLearning #ComputerVision #NLP

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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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πŸ”– 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

#ReinforcementLearning #TensorFlow #MachineLearning #DeepLearning #AI #Tech

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Get a job or employment opportunity by using our smart bot that connects the right person to the right job.

After using the bot, click the Find Job button.

@UdemySybot
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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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