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adapt-image-models

[ICLR'23] AIM: Adapting Image Models for Efficient Video Action Recognition

awesome-courses

:books: List of awesome university courses for learning Computer Science!

awesome-incremental-learning

Awesome Incremental Learning

awesome-transformer-attention

An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites

awesome-visual-transformer

Collect some papers about transformer with vision. Awesome Transformer with Computer Vision (CV)

bigdl

BigDL: Distributed Deep Learning Library for Apache Spark

captcha-yolo

Breaking CAPTCHA with deep learning

captcha_breaker

captcha_decision

Project of nn_based decision algorithms.

cnn_captcha

use cnn recognize captcha by tensorflow. 本项目针对字符型图片验证码，使用tensorflow实现卷积神经网络，进行验证码识别。

cs230-code-examples

Code examples in pyTorch and Tensorflow for CS230

deep-learning-project-template

A best practice for deep learning project template architecture.

deeplearning

demo09

打码

fedlab-benchmarks

Standard federated learning implementations in FedLab and FL benchmarks.

fedweit

This is an official Tensorflow-2 implementation of Federated Continual Learning with Inter-Client Weighted Transfer

flaml

A fast and lightweight AutoML library.

helloworld

First GitHub Project

leaf

Leaf: A Benchmark for Federated Settings

legodnn

mpi4py

Python bindings for MPI

niid-bench

Federated Learning on Non-IID Data Silos: An Experimental Study

optuna

A hyperparameter optimization framework

pfedme

Personalized Federated Learning with Moreau Envelopes (pFedMe) using Pytorch (NeurIPS 2020)

rainbow

Rainbow: Combining Improvements in Deep Reinforcement Learning

rotnet

servicecomposition

bucket and minicon service composition

servicecomposition-1

spark-scheduler-configuration-optimizer

A Spark cluster scheduler configuration optimizer based on Rainbow and driven by deep reinforcement learning.

tdd-tetris-tutorial

Tutorial for learning TDD. You make a Tetris game by writing code to pass the test cases. See README for instructions.