Machine Learning
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Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.

Admin: @HusseinSheikho || @Hussein_Sheikho
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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
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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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📚 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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🔖 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
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🧲 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.

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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 🤩
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Forwarded from Free Online Courses
🎓 Deep Learning for Images with PyTorch: CNNs to GANs

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This advanced computer vision course delivers a hands-on exploration of PyTorch across all major vision tasks. From Convolutional Neural Networks (CNNs) for image classification to advanced segmentation masks and Generative Adversarial Networks (GANs), it prepares practitioners for complex computer vision engineering tasks.
Who It's For
Advanced PyTorch practitioners, computer vision engineers, and machine learning research engineers seeking deep technical expertise in image processing and synthesis.
Key Takeaways
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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