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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
📖 "A Little Book on the Fundamentals of Generative AI" - an intuitive introduction to the mathematics:
arxiv.org/pdf/2605.29713
#GenerativeAI #Mathematics #DeepLearning #AIResearch #MachineLearning #arXiv
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If you're just starting to learn machine learning and want to delve deeper into the mathematics required for machine learning and deep learning, I recommend trying this platform. It's something like LeetCode for machine learning.
This is not an advertisement: I personally used it and decided to share it with you.
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Uniface
Automate face detection, recognition, and analysis of key facial landmarks with the Uniface Python library.
https://github.com/yakhyo/uniface
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Here's a small fact about Python 🐍
The := operator is called the "walrus" because the symbols resemble the eyes and tusks of a walrus 🦭
It was introduced in Python 3.8 and allows you to assign a value to a variable and use it directly within the expression at the same time.
For example:
while (line := input("Say something: ")) != "quit":
print(f"You said: {line}")input(), and then check it.:= operator in your code?
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If you fully understand this article, you will understand inference better than 90% of people.
And by the way, this is just the first material in the AI Performance Engineering repository.
It's scary to think how much knowledge is contained in the rest.
https://github.com/wafer-ai/gpu-perf-engineering-resources
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🎓 Deep Learning for Text with PyTorch: NLP & Transformers
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…
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Professor Steve Branton from the Mechanical Engineering Department at the University of Washington has uploaded a complete course on control theory for master's and doctoral students to YouTube. It's free.
The course is called Control Bootcamp.
It covers topics such as linear systems, stability and eigenvalues, controllability and observability, pole placement, the Kalman filter, LQR/LQG, robust control, and MPC – all explained sequentially with examples in Matlab.
Branton is the Boeing Professor of AI & Data-Driven Engineering at the University of Washington. He holds a bachelor's degree in mathematics from Caltech, with a specialization in control and dynamical systems, and a Ph.D. in mechanical and aerospace engineering from Princeton.
Playlist: https://youtube.com/playlist?list=PLMrJAkhIeNNR20Mz-VpzgfQs5zrYi085m
📚 This is probably one of the best technical books on how large language models are trained at scale:
> GPU memory and profiling
> Breaking down computations into blocks, kernel fusion, and FlashAttention
> Data parallelism, tensor parallelism, pipeline parallelism, and context parallelism
I've already read the free online version, but I still had to buy a physical copy for my library. 📖
You can also read it for free on Hugging Face:
https://huggingface.co/spaces/nanotron/ultrascale-playbook
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🔖Computer Science Fundamentals from MIT
We found the textbook Mathematics for Computer Science – covering the mathematics that underlies algorithms and computer science.
Logic, graphs, combinatorics, probability, induction, recurrence relations, and discrete structures – all in one place.
⛓️ Link to the textbook
https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-spring-2015/mit6_042js15_textbook.pdf
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"Linear Algebra with Applications" is a free and comprehensive textbook that introduces the computational, theoretical, and applied aspects of linear algebra.
The book covers topics such as systems of linear equations, matrices, determinants, vector spaces, linear transformations, eigenvalues and eigenvectors, diagonalization, inner product spaces, orthogonality, and many more. The explanations are accompanied by over 330 worked examples, exercises, and practical applications in geometry, electrical networks, dynamic systems, probability theory, and optimization.
A particularly interesting section discusses how Google's PageRank algorithm uses the dominant eigenvector to rank web pages. The links between websites are represented as a connectivity matrix, and the components of its dominant eigenvector provide an estimate of the relative importance of each page.
This is a very clear example of how an apparently abstract idea from linear algebra can underlie a real-world technology used on a massive scale.
The 2023 edition is available under a Creative Commons license. This is another excellent resource that is worth keeping as a reference.
https://collection.bccampus.ca/textbook/qTj4b4Ey
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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Pandas vs Polars — 14-section course cheatshee
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OpenAI researcher Alice Liu went through 57 interviews before being hired, and then openly shared her entire preparation and job search journey.
If you're preparing for Research Scientist or MTS positions, this is one of the most comprehensive resources available.
You can use her notes directly, or simply use the list of topics to study them yourself. This kind of information is rarely published.
Notes on LLMs:
https://alisawuffles.notion.site/alisa-s-book-of-llms
Mathematics:
https://alisawuffles.notion.site/math-notes
Analysis of the job search and interview process:
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Struggling with subjects like DSA, DBMS, OS or Computer Networks? These free resources can make learning much easier
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• Detailed lectures on OS, CN, DBMS, DSA and other CSE subjects
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• Structured roadmaps for different tech careers
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👨🏻💻 This file is a "comprehensive cheat sheet" for data scientists. Whenever you forget how to join data or customize a chart while coding, just refer to it.
⬅️ Chapter 1: All NumPy functions for creating arrays and broadcasting.
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This repository contains Jupyter notebooks for the O'Reilly book "Transformers: The Definitive Guide."
It includes code for computer vision tasks, time series analysis, audio processing, and reinforcement learning.
https://github.com/Nicolepcx/transformers-the-definitive-guide
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You know the shape of the script before you open the editor. The hour goes to argparse, a retry wrapper, a rate limiter you have written eleven times already.
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▫️ describe a tool in a sentence and it writes, runs and returns the working script
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Читать полностью…
Roadmap for those who want to become a robotics engineer:
* Programming → Python, C++
* Mathematics → Linear algebra, calculus, probability theory
* Electronics → Sensors, motors, power systems
* Embedded systems → Microcontrollers, real-time operating systems, hardware interaction
* Control theory → PID controllers, modeling, stability
* Mechanics → Kinematics, dynamics, CAD systems
* Linux → Terminal, networking, debugging
* Robotics software → ROS 2
* Simulation → Gazebo, Isaac Sim
* Environmental perception → Computer vision, LiDAR, sensor data fusion
* Localization → Kalman filters, SLAM
Motion planning → A, RRT, trajectory generation
* Manipulator control → Inverse kinematics, object grasping
* AI for robotics → Reinforcement learning
* Building your own robots → Drones, rovers, robotic arms
* Autonomy → Perception → Planning → Control
* Deployment on real devices → Edge computing, AI directly on board
* Industrial robotics → PLCs, production automation
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
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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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