👍 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 — let's learn here.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/resources/lecture-videos/

tags: #python #deeplearning

➡ @codeprogrammer

🗂 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 — all for free.

Inside:

• basics of Python for data analysis;
• machine learning and working with models;
• pandas, SQL, visualization;
• advanced techniques and practical cases.

Each course takes just 3–5 hours and immediately provides practical knowledge for work.

➡ Link to the platform

tags: #ML #DEEPLEARNING #AI

➡ https://t.me/CodeProgrammer

🤖 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 – 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 – 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.me/CodeProgrammer

Stop asking "CNN or VLM?" — the answer is both. 🤔

Everyone's talking about Vision Language Models replacing traditional computer vision. 📢
Here's the reality: they're not replacing anything. They're expanding what's possible. 🚀
CNNs are excellent at precise perception — detecting, localizing, classifying fixed objects at high speed and low cost. 🎯
Vision Language Models are better at interpretation — answering open-ended questions about a scene that you can't define as fixed labels in advance. 🧠
The smartest production systems combine both:
→ A lightweight CNN runs first (fast, cheap) ⚡️
→ A VLM handles the complex reasoning (flexible, expensive) 💎
This is the difference between giving machines eyes 👁 vs giving them the ability to talk about what they see. 🗣
Dr. Satya Mallick breaks it down in under 2 minutes. 👇
#ComputerVision #AI #MachineLearning #VisionLanguageModel #DeepLearning #OpenCV #AIEngineering

https://t.me/CodeProgrammer ✅