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

"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

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

Try it, it's free, your AI assistant

📚 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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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