🔖 A Comprehensive Resource for Machine Learning Practice

The platform offers:

🫡 1200+ machine learning tasks;
🫡 Mathematics, from linear algebra to PCA;
🫡 Deep Learning, NLP, and Computer Vision;
🫡 Labs with real datasets;
🫡 Learning paths and interview preparation;
🫡 Projects using GPT, RL, and CUDA.

Almost everything can be accessed directly in your browser, and basic tasks are available for free.

⛓️ Link to the platform
https://enghusseinsheikho.substack.com/p/a-comprehensive-resource-for-machine?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

#MachineLearning #DeepLearning #DataScience #AI #Coding #Tech

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"Matrix Differential Calculus for Machine Learning and Beyond" is a free course of lecture materials from MIT, focusing on the mathematics of derivatives of vectors and matrices.

The materials cover gradients, Jacobians, derivatives of matrix functions, differentials, derivatives of matrix products, inverse matrices, and determinants, Kronecker products, optimization, and automatic differentiation. It also explores the application of these methods in machine learning.

In my opinion, this is a valuable resource for those who are already familiar with the basics of linear algebra and mathematical analysis and want to understand how differentiation is generalized to vectors and matrices and applied in modern optimization problems.

https://ocw.mit.edu/courses/18-s096-matrix-calculus-for-machine-learning-and-beyond-january-iap-2023/mit18_s096iap23_lec_full.pdf

https://t.iss.one/MachineLearning9

You can literally enter a sentence and see how the Transformer processes it.

This interactive tool runs a GPT-like model directly in your browser and allows you to trace the entire process:

Tokenization → Embeddings → Q/K/V → Attention Mechanism → MLP → Logits → Probabilities of the next token.

You can explore attention maps, intermediate values, and adjust parameters like temperature, top-k, and top-p to see how they affect the generation.

If Transformers still seem like a collection of blocks connected by arrows, you should definitely try this.

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

https://t.iss.one/DataAnalyticsX

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📚 7 Best Websites to Learn Computer Science Subjects for Free

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Agentic AI is the future.

If you want to become god-level with AI agents, save these 10 GitHub repos:

1 Awesome LLM Apps

🔗 https://github.com/Shubhamsaboo/awesome-llm-apps

2 Generative AI for Beginners

🔗 https://github.com/microsoft/generative-ai-for-beginners

3 LLM Course

🔗 https://github.com/mlabonne/llm-course

4 Prompt Engineering Guide

🔗 https://github.com/dair-ai/Prompt-Engineering-Guide

5 AI Agents for Beginners

🔗 https://github.com/microsoft/ai-agents-for-beginners

6 AI Agents Projects

🔗 https://github.com/ashishpatel26/500-AI-Agents-Projects

7 Hugging Face Agents Course

🔗 https://github.com/huggingface/agents-course

8 Awesome AI Agents

🔗 https://github.com/e2b-dev/awesome-ai-agents

9 GenAI Agents

🔗 https://github.com/NirDiamant/GenAI_Agents

10 Agents Towards Production

🔗 https://github.com/NirDiamant/agents-towards-production

Anything else you'd add?