Excellent free courses on neural networks from Nvidia— the company decided to share knowledge that usually costs 90 dollars.

Here's everything important: video processing, app development, robotics, and much more. An electronic certificate is issued upon completion of the training.

We gain useful knowledge —
https://developer.nvidia.com/join-nvidia-developer-program

https://news.1rj.ru/str/CodeProgrammer 🌟

⚡️ MIT has released a full course on Deep Learning - for free

MIT OpenCourseWare has published the course 6.7960 Deep Learning (Fall 2024) — one of the most relevant and practical university courses on modern deep learning.

It includes full-fledged lectures at a top-university level, available for free.

What's in the course

- Fundamentals of deep learning and architectures 
- Transformers and modern models 
- Generative AI 
- Self-supervised learning 
- Scaling laws 
- Diffusion and generative models 
- RL and reinforcement learning 
- Practical analyses of modern approaches 

The lectures are led by MIT professors and researchers working with cutting-edge technologies.

Why it's valuable

This is not a basic course for beginners. 
This is material at the level of:
- ML engineers 
- researchers 
- developers of AI systems 

The course reflects the current state of the industry and explains how people who create modern models think.

It's perfect if you:
- already know Python and the basics of ML 
- want to transition to Deep Learning 
- work with LLMs / AI 
- want a systematic understanding instead of individual tutorials 

If you want FAANG / Research-level knowledge - learn from MIT.

https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/video_galleries/lecture-videos/

https://news.1rj.ru/str/CodeProgrammer ✅

10 GitHub Repositories to Master System Design

Want to move beyond drawing boxes and arrows and actually understand how scalable systems are built? These GitHub repositories break down the concepts, patterns, and real-world trade-offs that make great system design possible.

Most engineers encounter system design when preparing for interviews, but in reality, it is much bigger than that. System design is about understanding how large-scale systems are built, why certain architectural decisions are made, and how trade-offs shape everything from performance to reliability. Behind every app you use daily, from messaging platforms to streaming services, there are careful decisions about databases, caching, load balancing, fault tolerance, and consistency models.

What makes system design challenging is that there is rarely a single correct answer. You are constantly balancing cost, scalability, latency, complexity, and future growth. Should you shard the database now or later? Do you prioritize strong consistency or eventual consistency? Do you optimize for reads or writes? These are the kinds of questions that separate surface-level knowledge from real architectural thinking.

The good news is that many experienced engineers have documented these patterns, breakdowns, and interview strategies openly on GitHub. Instead of learning only through trial and error, you can study real case studies, curated resources, structured interview frameworks, and production-grade design principles from the community.

In this article, we review 10 GitHub repositories that cover fundamentals, interview preparation, distributed systems concepts, machine learning system design, agent-based architectures, and real-world scalability case studies. Together, they provide a practical roadmap for developing the structured thinking required to design reliable systems at scale.


Read: https://www.kdnuggets.com/10-github-repositories-to-master-system-design

https://news.1rj.ru/str/DataScienceM ✅

Python Cheat Sheet: Beginner to Expert Guide

This #Python cheat sheet covers basics to advanced concepts, regex, list slicing, loops and more. Perfect for quick reference and enhancing your coding skills.

Read: https://www.almabetter.com/bytes/cheat-sheet/python

https://news.1rj.ru/str/DataScience4 ✉️

ML Engineer, LLM Engineer, take note: TorchCode

A platform with practice tasks for basic implementations in PyTorch and questions on Transformer, which are often encountered in interviews.

→ Gathers in 39 structured tasks typical for #ML #interviews - implementations of operators, modules, and architectures in #PyTorch.
→ Provides auto-checking, gradient checking, time measurement, and instant feedback, so that the practice more closely resembles #LeetCode for interviews.
→ Built on the basis of Jupyter Notebook, while supporting one-click reset, hints, reference solutions, and progress tracking.
→ Covers such frequent topics as ReLU, Softmax, LayerNorm, Attention, RoPE, Flash Attention, #LoRA, $MoE, and others.
→ Supports online mode via Hugging Face Spaces, opening individual tasks in #Google #Colab, and local launch via #Docker.

👉 https://github.com/duoan/TorchCode

🧠 Python libraries for AI agents - complexity of learning 🔥

🟢 Easy
• LangChain
• tool calling
• agent memory
• simple agents

• CrewAI
• agents with roles
• collaboration of several agents

• SmolAgents
• lightweight agents
• quick experiments

🟡 Medium
• LangGraph
• stateful workflow
• agent orchestration

• LlamaIndex
• RAG pipelines
• data indexing
• knowledge agents

• OpenAI Agents SDK
• tool integrations
• agent workflows

• Strands
• agent orchestration
• task coordination

• Semantic Kernel
• skills / plugins
• AI process orchestration

• PydanticAI
• typed LLM applications
• structured agent workflows

• Langroid
• message exchange between agents
• interaction with tools

🔴 Difficult
• AutoGen
• multi-agent dialogues
• autonomous agent cooperation

• DSPy
• programmable prompting
• optimization of LLM pipelines

• A2A
• agent-to-agent protocol
• distributed agent systems

https://news.1rj.ru/str/CodeProgrammer ✅

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://news.1rj.ru/str/addlist/8_rRW2scgfRhOTc0

✅ https://news.1rj.ru/str/Codeprogrammer

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