Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
- programming
- development
- design
- design-system
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Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
A list of useful payloads and bypass for Web Application Security and Pentest/CTF
Translated《动手学深度学习》 (Dive into Deep Learning): for Chinese readers, runnable, and open to discussion. The Chinese and English editions are used for teaching at more than 500 universities in more than 70 countries.
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
Learn AI Engineering! Learn it. Build it. Ship it for others.
A curated list of awesome Machine Learning frameworks, libraries and software.
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
A collection of learning resources for curious software engineers
Summer 2027 software engineering, data science, AI, quant, product management, and hardware internship postings. Updated daily by Simplify and Pitt CSC.
A visual, example-driven guide to Claude Code — from basic concepts to advanced agents, with copy-paste templates that bring immediate value.
TranslatedAiLearning: Data Analysis + Machine Learning in Action + Linear Algebra + PyTorch + NLTK + TF2
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
TranslatedLatest summary for 2025: technical interview questions from Alibaba, Tencent, Baidu, Meituan, Toutiao and others, along with answers and a compilation of analysis from expert question-setters.
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
PyTorch Tutorial for Deep Learning Researchers
Machine Learning Systems: Foundations, Scaling, Agentic AI, and Physical AI (Vols I–IV) • Harvard CS249r | https://mlsysbook.ai
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
deep learning for image processing including classification and object-detection etc.
Translated⚡️ IPTV live source auto-update tool: automatically collects, validates, speed-tests, and generates playable results. Supports M3U/TXT/API output, custom channels, IPv4/IPv6, Docker, GitHub Actions, and multi-platform deployment via CLI and GUI
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
Translated100-Days-Of-ML-Code Chinese version
Best Practices on Recommendation Systems
A collection of projects designed to help developers quickly get started with building deployable applications using the Claude API