The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
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Learn AI Engineering! Learn it. Build it. Ship it for others.
Curso para aprender el lenguaje de programación Python desde cero y para principiantes. 100 clases, 44 horas en vídeo, código, proyectos y grupo de chat. Fundamentos, frontend, backend, testing, IA...
deep learning for image processing including classification and object-detection etc.
- pytorch
- tensorflow2
- classification
- object-detection
Translated100-Days-Of-ML-Code Chinese version
- machine-learning
- python
- 100-days-of-ml-code
- chinese-simplified
AWS zero to hero repo for devops engineers to learn AWS in 30 Days. This repo includes projects, presentations, interview questions and real time examples.
Curso para aprender los fundamentos del lenguaje SQL y bases de datos relacionales desde cero y para principiantes.
- basesdedatos
- database
- mysql
- postgresql
"Context engineering is the delicate art and science of filling the context window with just the right information for the next step." — Andrej Karpathy. A frontier, first-principles handbook inspired by Karpathy and 3Blue1Brown for moving beyond prompt engineering to the wider discipline of context design, orchestration, and optimization.
A collection of machine learning examples and tutorials.
A multi-module course teaching everything you need to know about using GitHub Copilot as an AI Peer Programming resource.
The repository host gives no description.
learn LLM inference system on Apple Silicon for systems engineers: build a tiny vLLM + Qwen
- course
- large-language-model
- llm
- python
The repository host gives no description.
Learn Python from DevOps Engineer point of you.
TranslatedThis is a step-by-step guide to getting started with AI/LLM large models, including tutorials and demo code, taking you from APIs to local large model deployment and fine-tuning. The code files come with Kaggle or Colab online versions, so you can learn even without a graphics card. The project also has a small code playground 🎡 where you can try out some interesting AI scripts. It also includes complete Chinese mirror assignments for Hung-Yi Lee's (HUNG-YI LEE) 2024 Introduction to Generative AI course.
Translated"Large Language Models" (《大语言模型》), authors: Zhao Xin, Li Junyi, Zhou Kun, Tang Tianyi, Wen Jirong
- artificial-intelligence
- deep-learning
- deep-neural-networks
- deep-reinforcement-learning
🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 12 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴
- aws
- bytewax
- comet-ml
- generative-ai
Open-source Python challenge courses — your AI coding agent teaches, verify.py decides when you're done.
- python
- learning-python
- python-course
- claude-code
Synapsa Commons: free, hands-on AI courses that run anywhere (Colab, Kaggle, Binder, Codespaces, Jupyter). EU AI Act conformity evidence, model validation, predictive maintenance, document extraction, retrieval and RAG, AI agents and MCP, a simulated humanoid. Every lesson autograded. From the team building Synapsa.
Simple PyTorch Tutorials Zero to ALL!
- deeplearning
- pytorch
- python
- tutorial
Follow along with my AI Agents Masterclass videos! All of the code I create and use in this series on YouTube will be here for you to use and even build on top of!
Student version of Assignment 1 for Stanford CS336 - Language Modeling From Scratch
A no-fluff and highly practical masterclass that reignites engineering curiosity and helps SDE-2, SDE-3, and above become great at designing, implementing, and shipping scalable, fault-tolerant, and highly available systems at the workplace.
- system-design-project
- system-design
- system-design-interview
- system-design-questions
深度学习与PyTorch入门实战视频教程 配套源代码和PPT
- deep-learning
- pytorch
- convolutional-neural-networks
- recurrent-neural-networks