Tianle Zhang's Projects
Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"
🧑🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
😎 A curated list of awesome Jupyterlab extension projects. 🌠 Detailed introduction with images.
Drawing Bayesian networks, graphical models, tensors, and technical frameworks and illustrations in LaTeX.
【干货】史上最全的PyTorch学习资源汇总
Certify probabilistic robustness againt functional threat model
Foreign language reading and translation assistant based on copy and translate.
中南大学学位论文LaTeX模板
CVPR 2020 论文开源项目合集
cvpr2020/cvpr2019/cvpr2018/cvpr2017 papers,极市团队整理
This project reproduces the book Dive Into Deep Learning (www.d2l.ai), adapting the code from MXNet into PyTorch.
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
A MNIST-like fashion product database. Benchmark :point_right:
刷算法全靠套路,认准 labuladong 就够了!English version supported! Crack LeetCode, not only how, but also why.
starter from "How to Train a GAN?" at NIPS2016
Simple and efficient pytorch-native transformer text generation in <1000 LOC of python.
《Hello 算法》:动画图解、一键运行的数据结构与算法教程,支持 Java, C++, Python, Go, JS, TS, C#, Swift, Rust, Dart, Zig 等语言。
Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"
Keras Temporal Convolutional Network.
My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (1000+ slides) 我不间断更新的机器学习,概率模型和深度学习的讲义(1000+页)和视频链接
MatConvNet: CNNs for MATLAB
Train your own data with MatConvNet
it contains all the MATLAB demo code associated with my machine learning notes
For easy metric logging and visualization
Models and examples built with TensorFlow
The code on deep learning.
Code for reproducing results of NIPS 2014 paper "Semi-Supervised Learning with Deep Generative Models"