Machine Learning

🤔 Mathos AI — a neural network for solving and learning mathematics!

This AI service helps you break down mathematical problems step-by-step, with explanations for each action. You can enter the problem as text, take a photo, or upload a PDF — Mathos will recognize the problem, suggest a solution, and, if necessary, create a graph. You can request not a ready-made answer, but only a hint, to continue solving the problem yourself.

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https://t.me/MachineLearning9

The correct way to learn is like this:

You simply need to solve problems and work on projects.

This approach was used in one of the best books on the fundamentals of statistics – and, incidentally, one of the few that I actually read.

It's very simple:

You read a chapter.
You solve all the problems related to that topic.

https://www.statlearning.com/

🔖 Python Reference for Data Science and Machine Learning

PY-DS-ML provides practical resources on 30 popular Python libraries for data analysis and machine learning.

You can quickly find the commands, syntax, and examples you need without having to search through extensive documentation.

It includes a search function, organization by difficulty level, cheat sheets, and checklists.

Link: https://py-ds-ml.ru/

#russian #ML

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🔖 Free book on graph theory

"Introduction to Graph Theory" by Darij Grinberg is an advanced resource with theorems, an algebraic approach, and plenty of exercises.

You can read it for free with an AI tutor.

⛓ Link to the book
https://chapterpal.com/ebook/473a1c1b-c584-4430-b986-cd9c57085e67

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🎓 Deep Learning for Images with PyTorch: CNNs to GANs

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Advanced PyTorch practitioners, computer vision engineers, and machine learning research engineers seeking deep technical expertise in image processing and synthesis.
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• CNNs & Object Detection: Train CNNs for binary and multi-class classification, leverage pre-trained models, and evaluate object detection using bounding boxes.
…

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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 🤩

🧲 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
▫️ ships a mini-app inside Telegram — a form, a converter, a dashboard, no deploy and no hosting
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Machine Learning pinned a photo

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🔖 Learning Mathematics — How to Stop Being Afraid of Math

The author explains that mathematical thinking is not an innate talent, but a skill that develops gradually through practice, time, and systematic work.

Useful reading for those who are building their math foundation for Data Science and Machine Learning.

⛓Link to the book

📚 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

"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