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

Admin: @HusseinSheikho || @Hussein_Sheikho
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๐Ÿ“š 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. ๐Ÿ“–

You can also read it for free on Hugging Face:

https://huggingface.co/spaces/nanotron/ultrascale-playbook

#LLM #AI #MachineLearning #TechBooks #DataScience #Coding

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๐Ÿ”– Learning Mathematics โ€” 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.

โ›“Link to the book
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Perfect for Software Developer Jobs, IT Internships, and Python Projects practice.

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๐Ÿงฒ 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.

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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 ๐Ÿคฉ
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Forwarded from Free Online Courses
๐ŸŽ“ Deep Learning for Images with PyTorch: CNNs to GANs

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๐Ÿ“ Course Details:
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
โ€ข 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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๐Ÿ”– 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.

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