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zclllyybb's Projects

acmer-info icon acmer-info

The index for acmer group. Licensed under CC BY-NC-SA 4.0

addons icon addons

Useful extra functionality for TensorFlow 2.x maintained by SIG-addons

awesome-neural-logic icon awesome-neural-logic

Awesome Neural Logic and Causality: MLN, NLRL, NLM, etc. 因果推断,神经逻辑,强人工智能逻辑推理前沿领域。

capsnet-keras icon capsnet-keras

A Keras implementation of CapsNet in NIPS2017 paper "Dynamic Routing Between Capsules". Now test error = 0.34%.

causal-learn icon causal-learn

Causal Discovery for Python. Translation and extension of the Tetrad Java code.

clang-uml icon clang-uml

Customizable automatic UML diagram generator for C++ based on Clang.

cpufp icon cpufp

A CPU tool for benchmarking the peak of floating points

deep-rl-keras icon deep-rl-keras

Keras Implementation of popular Deep RL Algorithms (A3C, DDQN, DDPG, Dueling DDQN)

deepmatch icon deepmatch

A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors which can be used for ANN search.

doris icon doris

Apache Doris is an easy-to-use, high performance and unified analytics database.

easy-rl icon easy-rl

强化学习中文教程,在线阅读地址:https://datawhalechina.github.io/easy-rl/

external-attention-pytorch icon external-attention-pytorch

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

fitlog icon fitlog

fitlog是一款在深度学习训练中用于辅助用户记录日志和管理代码的工具

gym icon gym

A toolkit for developing and comparing reinforcement learning algorithms.

interpret icon interpret

Fit interpretable models. Explain blackbox machine learning.

it_book icon it_book

本项目收藏这些年来看过或者听过的一些不错的常用的上千本书籍,没准你想找的书就在这里呢,包含了互联网行业大多数书籍和面试经验题目等等。有人工智能系列(常用深度学习框架TensorFlow、pytorch、keras。NLP、机器学习,深度学习等等),大数据系列(Spark,Hadoop,Scala,kafka等),程序员必修系列(C、C++、java、数据结构、linux,设计模式、数据库等等)

keras-gan icon keras-gan

Keras implementations of Generative Adversarial Networks.

keras-one-cycle icon keras-one-cycle

Implementation of One-Cycle Learning rate policy (adapted from Fast.ai lib)

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