Repository Index

Crack LeetCode, not only how, but also why.

• leetcode
• algorithms
• interview-questions
• data-structures

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

• 30-days-of-python
• python
• flask
• github

🧑‍🏫 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, ... 🧠

• deep-learning
• deep-learning-tutorial
• pytorch
• gan

Translated"Code Random Thoughts" (代码随想录) LeetCode problem-solving guide: the order for working through 200 classic problems, with 600,000 characters of detailed illustrated explanations, video analysis of difficult points, and over 50 mind maps. Supports multiple language versions including C++, Java, Python, Go, and JavaScript, so learning algorithms is never confusing again! 🔥🔥 Come take a look, and you will wish you had met it sooner! 🚀

• leetcode
• programmer
• interview
• offer

🚀✨ Help beginners to contribute to open source projects

• open-source
• tutorial
• contribution
• beginner

100 Days of ML Coding

• machine-learning
• machine-learning-algorithms
• infographics
• tutorial

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

• tensorflow
• tutorial
• examples
• deep-learning

What the f*ck Python? 😱

• python
• wats
• snippets
• wtf

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.

• rag
• tutorials
• langchain
• llama-index

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.

• python
• machine-learning
• deep-learning
• data-science

:snowman: Possibly the smallest compiler ever

Best Practices on Recommendation Systems

• machine-learning
• recommender
• ranking
• deep-learning

🎮 A step-by-step guide to implementing SSAO, depth of field, lighting, normal mapping, and more for your 3D game.

• shaders
• 3d
• graphics
• panda3d

Translated[Lumina Embodied AI Community] Embodied AI technology guide Embodied-AI-Guide

• embodied-ai
• guide
• tutorial
• robotics

TranslatedAlgorithm Patterns — the most scientific way to practice, the fastest path to an offer. You deserve it~ Algorithm templates, the most scientific way to practice problems, the fastest path to practice problems, you deserve it~

• leetcode
• leetcode-go
• algorithm
• algo

TranslatedChinese tutorial on reinforcement learning (Mushroom Book 🍄), read online at: https://datawhalechina.github.io/easy-rl/

• deep-reinforcement-learning
• reinforcement-learning
• dqn
• ppo

TranslatedAirport recommendations, Clash / V2ray client configuration, and proxy tool guides

• clash
• documentation
• mihomo
• proxy

FFmpeg libav tutorial - learn how media works from basic to transmuxing, transcoding and more. Translations: 🇺🇸 🇨🇳 🇰🇷 🇪🇸 🇻🇳 🇧🇷 🇷🇺

• ffmpeg
• ffmpeg-libraries
• libav
• tutorial

TranslatedA programmer who loves cooking and is determined to use animations to explain algorithms in plain, easy-to-understand terms. My interview website: www.chengxuchu.com

• leetcode
• offer
• algorithms
• base

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

• deep-learning
• mit
• self-driving-cars
• deep-reinforcement-learning

Toturials coming with the "data science roadmap" picture.

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

:earth_americas: machine learning tutorials (mainly in Python3)

• machine-learning
• data-science
• jupyter-notebook
• python3

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

• machine-learning
• deep-learning
• lstm
• human-activity-recognition