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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Diving deep into Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP. ๐Ÿค–๐Ÿง 

Lectures: ๐ŸŽ“๐Ÿ“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! ๐Ÿ˜Š Would love more feedback and ideas!

It's a free interactive graph visualizer for learning about the architectures of open source AI models! ๐Ÿš€

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

๐ŸŒŸ hfviewer.com

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๐Ÿ”– 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.

โ›“๏ธ Link to GitHub
https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow

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

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Maths, CS & AI Compendium: A free textbook for aspiring AI/ML engineers

๐Ÿš€ A large open-source compendium on mathematics, computer science, and AI has gone viral on GitHub. The project already has around 6.3K stars.

๐Ÿ“š The author positions it as a "non-traditional textbook" for practitioners: less dry notation, more intuition, connections between topics, and real-world context.

๐Ÿ“– 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

๐Ÿค– 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.

๐Ÿ’ก This is a great resource for those who want to not just "learn ML," but to build a solid foundation: mathematics โ†’ CS โ†’ ML systems โ†’ modern AI.

๐Ÿ”— GitHub: https://github.com/HenryNdubuaku/maths-cs-ai-compendium

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Stop searching for generative AI resources one category at a time. ๐Ÿš€

Awesome Generative AI is a curated directory of generative AI projects and services for builders exploring the ecosystem. ๐ŸŒ

It helps you compare where to look next by organizing links and short descriptions across models, tools, agents, media, and learning resources. ๐Ÿ“Š

Key features:
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โ€ข Coding toolkit โ€“ find coding assistants, developer tools, playgrounds, and local LLM deployment options ๐Ÿ’ป
โ€ข Agent directory โ€“ scan autonomous agent projects and custom assistant resources ๐Ÿค–
โ€ข Multimodal map โ€“ explore image, video, audio, and music tools in dedicated sections ๐ŸŽจ๐ŸŽต
โ€ข Learning library โ€“ use recommended reading, milestones, courses, guides, and related lists to build context ๐Ÿ“š

Itโ€™s open-source (CC0-1.0 license). ๐Ÿ”“

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

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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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๐Ÿ†“ Free Video Course
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๐Ÿ”— Course Link

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Generative AI for Beginners
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AI Capabilities and Limitations
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Generative AI for Beginners
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๐Ÿ”— Course Link



Reading Materials

๐Ÿ“– Prompt Engineering Guide
๐Ÿ“– Awesome Generative AI (Curated Resource List)
๐Ÿ“– Generative AI: A Beginner's Guide
๐Ÿ“– Understanding Generative AI Capabilities
๐Ÿ“–Stanford HAI: 2025 AI Index Report
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๐Ÿ”– A useful training tool for Data Scientists ๐Ÿ“Š

๐Ÿซก Real-world tasks from IT companies;
๐Ÿซก SQL practice;
๐Ÿซก Python tasks;
๐Ÿซก Preparation for Data Science interviews.

โ›“ Link to the training tool
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๐Ÿท #DataScience #SQL #Python #InterviewPrep #TechTraining #DataAnalyst

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A Powerful Alternative to Pandas ๐Ÿš€

This is an optimized replacement for Pandas that can significantly speed up data processing without requiring major changes to your code. โš™๏ธ

To get started, simply replace a single import:

import fireducks.pandas as pd

Performance Benchmarks demonstrate speed improvements in various use cases. ๐Ÿ“ˆ

More: https://colab.research.google.com/drive/1UIokuJ4cytoiVSabRDqcziDXOan8bVua?usp=sharing

#Pandas #Python #DataScience #Performance #Fireducks #BigData

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