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xDiT: A Scalable Inference Engine for Diffusion Transformers (DiTs) with Massive Parallelism
A game theoretic approach to explain the output of any machine learning model.
🚀🚀 「大模型」2小时完全从0训练26M的小参数GPT！🌏 Train a 26M-parameter GPT from scratch in just 2h!
Neural Networks and Deep Learning, NUS CS5242, 2021
Graph Machine Learning course, Xavier Bresson, 2023
Main code for "Revisiting over-smoothing and over-squashing using the Ollivier-Ricci curvature" paper
A comprehensive collection of KAN(Kolmogorov-Arnold Network)-related resources, including libraries, projects, tutorials, papers, and more, for researchers and developers in the Kolmogorov-Arnold N…
Deep Graph Infomax (https://arxiv.org/abs/1809.10341)
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
PyTorch implementation of "Simple and Deep Graph Convolutional Networks"
High-performance, scalable time-series database designed for Industrial IoT (IIoT) scenarios
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…
A playbook for systematically maximizing the performance of deep learning models.
Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"
links to conference publications in graph-based deep learning
Put develop tools here, such as style check and editing
Mathematical derivation and pure Python code implementation of machine learning algorithms.
HugeCTR is a high efficiency GPU framework designed for Click-Through-Rate (CTR) estimating training
总结梳理自然语言处理工程师(NLP)需要积累的各方面知识，包括面试题，各种基础知识，工程能力等等，提升核心竞争力
📓 DeepLearning and CV notes.
Implement Statistical Learning Methods, Li Hang the hard way. 李航《统计学习方法》一书的硬核 Python 实现
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
Implementation and experiments of graph embedding algorithms.