Machine Learning with Python

Matrix Calculus for Machine Learning and Beyond! — a free ebook from MIT.

This is the 2025 MIT textbook by Alan Edelman, Steven G. Johnson, and Paige Bright.

The book directly connects matrix calculus to modern machine learning.

Inside:

*   Derivatives of matrices and vectors
*   Jacobian and Hessian
*   Matrix decompositions
*   Optimization
*   Differentiation in reverse mode
*   Backpropagation of error
*   Automatic differentiation
*   Derivatives through ODEs
*   Problems focused on machine learning

This is a comprehensive mathematical bridge between linear algebra, calculus, optimization, backpropagation, and machine learning.

Free ebook:
https://geni.us/Matrix-Calculus-Book

From machine learning and data visualization to time series and financial data.

This repository contains 920 open-source Python projects, categorized into 34 groups.

It's a great collection if you want to quickly find reliable libraries and tools for machine learning, data analysis, and related tasks, rather than searching everything manually on GitHub.

https://github.com/lukasmasuch/best-of-ml-python

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Machine Learning with Python pinned a video

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🚨 Cambridge has just released a real bombshell this time.

📚 A whole collection of classic textbooks on AI and machine learning is now available for free in PDF format.

If you want to really understand machine learning and don't want to waste money on overpriced courses, these ten books will be enough to build a very solid foundation.

From simple to complex.

1️⃣ Understanding Machine Learning

One of the best books for beginners. It covers the basic theoretical algorithms of machine learning.

🔗 https://cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf

2️⃣ Mathematical Foundations of Machine Learning

If you're not very confident in your math skills, I would start here.

🔗 https://mml-book.github.io/book/mml-book.pdf

3️⃣ Mathematical Analysis of Machine Learning Algorithms

A more in-depth look at the mathematical principles of machine learning algorithms.

🔗 https://tongzhang-ml.org/lt-book/lt-book.pdf

4️⃣ Theoretical Principles of Deep Learning

The theoretical foundations of deep learning and an understanding of why it all works.

🔗 https://arxiv.org/pdf/2106.10165

5️⃣ Neural Networks and Learning Machines

A systematic analysis of neural networks and the principles of their training.

🔗 https://arxiv.org/pdf/1901.05639

6️⃣ Graph Deep Learning

A good starting point for those who want to understand graph neural networks.

🔗 https://yaoma24.github.io/dlg_book/dlg_book.pdf

7️⃣ Machine Learning: A Probabilistic Perspective

It allows you to look at machine learning from a probabilistic and algorithmic perspective.

🔗 https://people.csail.mit.edu/moitra/docs/bookexv2.pdf

8️⃣ Probability Theory: Theory and Examples

Fundamental theory of probability. Very useful if you want to understand machine learning beyond the level of using ready-made libraries.

🔗 https://sites.math.duke.edu/~rtd/PTE/PTE5_011119.pdf

9️⃣ Fundamentals of Applied Probability

More focus on the practical application of probability theory.

🔗 https://sites.math.duke.edu/~rtd/EP4A/EP4A_April2021.pdf

🔟 Advanced Data Analysis

An advanced level for those who want to seriously improve their data analysis skills.

🔗 https://stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf

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🔖 A comprehensive resource on LLMs in one repository

We found an open-source course covering Transformers, LoRA, RAG, prompts, model editing, and other key topics.

After each chapter, you can immediately access the original sources – the authors have compiled papers and collections from arXiv.

⛓Link to GitHub
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Machine Learning with Python pinned a photo

Personal AI assistant in 5 minutes
No code. No card. Free 😳

Works in Telegram, WhatsApp, or Discord — just send it tasks by voice or text. It gets things done, not just tells you how to do them.

• reads and sends emails
• creates and edits Google Sheets
• uploads files to Google Drive
• works in Notion
• sends reminders
• generates PDFs, images, and videos
• actually makes life and work easier

✅ Create your personal AI assistant here →
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Tensor Algebra: A Small Concept That Has a Big Impact in AI 🧠

One thing I realized while learning deep learning is that tensors are everywhere. Whether you're working with TensorFlow, PyTorch, or building transformer models, almost everything revolves around tensor operations.

Although we often think of tensors as multi-dimensional arrays in machine learning, they're the structures that allow neural networks to efficiently represent and process complex data.

Here's a quick summary:
- Scalar (Rank 0): A single value
- Vector (Rank 1): A one-dimensional collection of values
- Matrix (Rank 2): A two-dimensional arrangement of values
- Tensor (Rank 3 or higher): A higher-dimensional representation used to model complex data

A few places where tensors show up every day:
- Images are represented as 3D tensors (Height × Width × Channels).
- Mini-batches become 4D tensors during model training.
- Transformer models process embeddings, attention scores, and hidden states as tensors throughout the network.
- Operations like matrix multiplication, broadcasting, reshaping, tensor contraction, and automatic differentiation power modern deep learning.

I created the infographic below as a simple visual reference while revisiting tensor algebra. I hope it's helpful for anyone learning deep learning or refreshing the fundamentals.

I'm curious. How did you first learn about tensors?
- Through mathematics?
- While using TensorFlow or PyTorch?
- During your first deep learning project?
- Or was there another resource that made the concept finally click?

I'd love to hear your experience and any resources you'd recommend for beginners. Looking forward to learning from your experiences and recommendations.

#DeepLearning #TensorFlow #PyTorch #AI #MachineLearning #Tensors

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