Machine Learning with Python

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📚 7 Best Websites to Learn Computer Science Subjects for Free

Struggling with subjects like DSA, DBMS, OS or Computer Networks? These free resources can make learning much easier

1. GeeksforGeeks 🧑‍💻
• DSA, DBMS, OS, CN and more
• Notes, articles, problems and tutorials
🔗 https://www.geeksforgeeks.org/

2. freeCodeCamp 🎓
• Programming, web development, Python and more
• Free courses with hands-on learning
🔗 https://www.freecodecamp.org/

3. CS50 by Harvard 🧠
• Excellent introduction to computer science
• Covers programming, algorithms, memory, databases and more
🔗 https://cs50.harvard.edu/

4. Neso Academy 📖
• Detailed lectures on OS, CN, DBMS, DSA and other CSE subjects
• Great for concept-based learning
🔗 https://www.youtube.com/@nesoacademy

5. W3Schools 🌐
• Learn HTML, CSS, JavaScript, SQL, Python and more
• Simple explanations with interactive examples
🔗 https://www.w3schools.com/

6. MIT OpenCourseWare 🎓
• University-level computer science courses
• Lectures, assignments and study material
🔗 https://ocw.mit.edu/

7. roadmap.sh 🗺
• Structured roadmaps for different tech careers
• Useful for planning what to learn next
🔗 https://roadmap.sh/

💾 Save this list for your next semester

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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😰 "Python for Data Science" Cheat Sheet

👨🏻‍💻 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.

⬅️ Chapter 2: Everything about Pandas, from selecting rows and columns (loc/iloc) to handling time series.

⬅️ Chapter 3: A complete catalog of charts (scatter plots, bar charts, histograms, pie charts).

⬅️ Chapter 4: The golden section! A summary table listing all the important commands in one place.

https://t.me/CodeProgrammer

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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

https://t.me/MachineLearning9 🤩

Machine Learning with Python pinned Deleted message

🧲 Your agent writes the tool. You keep the terminal closed.

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.

Create your own AI agent inside Telegram in about a minute, and create small tools with it right in the chat.

▫️ describe a tool in a sentence and it writes, runs and returns the working script
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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

Machine Learning with Python pinned a photo

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📚 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

https://t.me/CodeProgrammer ❤️🔰

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

"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