Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.

Admin: @HusseinSheikho || @Hussein_Sheikho
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๐Ÿ“ฑ A collection of videos on PyTorch and neural networks

This is not a full-fledged course with a unified program, but a collection of nine separate videos on PyTorch and neural networks gathered in one playlist.

Inside, there are materials of different levels and formats that are suitable for selective study of topics, practice, and a general understanding of the direction.

What's here:
๐Ÿฎ Introductory videos on PyTorch and the basics of neural networks;

๐Ÿฎ Practical analyses with code writing and project examples;

๐Ÿฎ Materials on computer vision and working with medical images;

๐Ÿฎ Examples of creating chat bots and models on PyTorch;

๐Ÿฎ Analyses of large language models and generative neural networks;

๐Ÿฎ Examples of training agents and reinforcement tasks;

๐Ÿฎ Videos from different authors without a general learning logic.
The collection is suitable for those who are already familiar with Python and want to selectively study PyTorch without a strict study plan โ€” get it here.

https://www.youtube.com/playlist?list=PLp0BA-8NZ4bhBNWvUBPDztbzLar9Jcgd-


tags: #pytorch #DeepLearning #python

โžก @CodeProgrammer
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โšก๏ธ All cheat sheets for programmers in one place.

There's a lot of useful stuff inside: short, clear tips on languages, technologies, and frameworks.

No registration required and it's free.

https://overapi.com/

#python #php #Database #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS

https://t.iss.one/CodeProgrammer โšก๏ธ
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Collection of books on machine learning and artificial intelligence in PDF format

Repo: https://github.com/Ramakm/AI-ML-Book-References

#MACHINELEARNING #PYTHON #DATASCIENCE #DATAANALYSIS #DeepLearning

๐Ÿ‘‰ @codeprogrammer
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๐Ÿ‘ A fresh deep learning course from MIT is now available publicly

A full-fledged educational course has been published on the university's website: 24 lectures, practical tasks, homework assignments, and a collection of materials for self-study.

The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.

A great opportunity to study deep learning based on the structure of a top university, free of charge and without simplifications โ€” let's learn here.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/resources/lecture-videos/

tags: #python #deeplearning

โžก @codeprogrammer
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๐Ÿ—‚ One of the best resources for learning Data Science and Machine Learning

Kaggle offers interactive courses that will help you quickly understand the key topics of DS and ML.

The format is simple: short lessons, practical tasks, and a certificate upon completion โ€” all for free.

Inside:
โ€ข basics of Python for data analysis;
โ€ข machine learning and working with models;
โ€ข pandas, SQL, visualization;
โ€ข advanced techniques and practical cases.


Each course takes just 3โ€“5 hours and immediately provides practical knowledge for work.

โžก Link to the platform

tags: #ML #DEEPLEARNING #AI

โžก https://t.iss.one/CodeProgrammer
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๐Ÿค– Best GitHub repositories to learn AI from scratch in 2026

If you want to understand AI not through "vacuum" courses, but through real open-source projects - here's a top list of repos that really lead you from the basics to practice:

1) Karpathy โ€“ Neural Networks: Zero to Hero 
The most understandable introduction to neural networks and backprop "in layman's terms"
https://github.com/karpathy/nn-zero-to-hero

2) Hugging Face Transformers 
The main library of modern NLP/LLM: models, tokenizers, fine-tuning 
https://github.com/huggingface/transformers

3) FastAI โ€“ Fastbook 
Practical DL training through projects and experiments 
https://github.com/fastai/fastbook

4) Made With ML 
ML as an engineering system: pipelines, production, deployment, monitoring 
https://github.com/GokuMohandas/Made-With-ML

5) Machine Learning System Design (Chip Huyen) 
How to build ML systems in real business: data, metrics, infrastructure 
https://github.com/chiphuyen/machine-learning-systems-design

6) Awesome Generative AI Guide 
A collection of materials on GenAI: from basics to practice 
https://github.com/aishwaryanr/awesome-generative-ai-guide

7) Dive into Deep Learning (D2L) 
One of the best books on DL + code + assignments 
https://github.com/d2l-ai/d2l-en

Save it for yourself - this is a base on which you can really grow into an ML/LLM engineer.

#Python #datascience #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS

https://t.iss.one/CodeProgrammer
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๐Ÿ—‚ A fresh deep learning course from MIT is now publicly available

A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study.

The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.

โžก๏ธ Link to the course

tags: #Python #DataScience #DeepLearning #AI
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How a CNN sees images simplified ๐Ÿง 

1. Input โ†’ Image breaks into pixels (RGB numbers)

2. Feature Extraction

ยท Convolution โ†’ Detects edges/patterns
ยท ReLU โ†’ Kills negatives, adds non-linearity
ยท Pooling โ†’ Shrinks data, keeps what matters

3. Fully Connected โ†’ Flattens features into meaning

4. Output โ†’ Probability scores: Cat? Dog? Car?

Why powerful: Learns hierarchically โ€” edges โ†’ shapes โ†’ objects

Pixels to predictions. That's it. ๐Ÿ‘‡

#DeepLearning #CNN #ComputerVision #AI

https://t.iss.one/CodeProgrammer
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