✂️ Automated high-quality background removal framework for an image using neural networks. ✂️

🏅 Collection of Kaggle Solutions and Ideas 🏅

12 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

Contains notebooks for the PAR tutorial at CVPR 2021.

🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch

汉语自然语言处理视频教程-开源学习资料

List of Data Science Cheatsheets to rule the world

Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images

FraudNE: a Joint Embedding Approach for Fraud Detection (IJCNN 2018)

Deep Structure Learning for Fraud Detection (ICDM 2018)

this is a collection of books and courses for machine learning.

A curated list of resources dedicated to text summarization

🔥 A collection of PyTorch notebooks for learning and practicing deep learning

This is the English version of the Image processing 100 questions.

A translation of J.M. Lilly's code for ridge and element analysis using generalized Morse wavelets into python

DataFountain第五届达观杯第4名方案

Workshop materials from Applied Machine Learning Days 2019 in Lausanne

flink learning blog. http:https://www.54tianzhisheng.cn/tags/Flink/

Diffusion Convolutional Recurrent Neural Network Implementation in PyTorch

3D computer vision incuding SLAM，VSALM，Deep Learning，Structured light，Stereo，Three-dimensional reconstruction，Computer vision，Machine Learning and so on

Implementation of Stochastic Beam Search using Fairseq

Real-time VIBE: Frame by Frame Inference of VIBE (Video Inference for Human Body Pose and Shape Estimation)

📚 Awesome CS Books/Series(.pdf by git lfs) Warehouse for Geeks, ProgrammingLanguage, SoftwareEngineering, Web, AI, ServerSideApplication, Infrastructure, FE etc. 💫 优秀计算机科学与技术领域相关的书籍归档

Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch

Rendered math (MathJax) with Slack's desktop client

IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz

Course webpage for COMP 790, (Deep) Learning from Limited Labeled Data

A curated list of resources for graph databases and graph computing tools

A benchmark for offline reinforcement learning.

PyTorch implementation for the Neural Logic Machines (NLM).