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1d-convolutional-variational-autoencoder

Convolutional Variational Autoencoder for classification and generation of time-series

adaptively-parametric-relu

The adaptively parametric ReLU is an activation function that performs non-identically for input samples.

ailearning

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

annotated-transformer

http://nlp.seas.harvard.edu/2018/04/03/attention.html

audio-digital-processing

数字信号处理大作业：Matlab实现语音分析：加噪声，频谱分析，滤波器等等（内附报告）【Matlab for speech analysis: add noise, spectrum analysis, filter, etc】

autoencoder

个人练习，自编码器及其变形（理论+实践）

awesome-awesome-machine-learning

A curated list of awesome lists across all machine learning topics. | 机器学习/深度学习/人工智能一切主题 (学习范式/任务/应用/模型/道德/交叉学科/数据集/框架/教程) 的资源列表汇总。

awesome-transfer-learning

Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)

awesome_deep_learning_interpretability

深度学习近年来关于神经网络模型解释性的相关高引用/顶会论文(附带代码)

basic-rotating-machine-vibration-analysis

These codes realize data transformation and simple data processing for fault diagnosis.

bayesian-deep-rul

Official implementation of https://arxiv.org/abs/1911.06256. Bayesian and frequentist deep learning models for remaining useful life (RUL) estimation implemented in PyTorch.

bayesian-mixture-of-gaussians

Numpy code for Coordinate Ascent Variational Inference for the Bayesian mixture of Gaussians model (Sec 10.2 of Bishop's PRML).

bayesian-neural-networks

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

bayesianoptimization

A Python implementation of global optimization with gaussian processes.

berkeley-stat-157

Homepage for STAT 157 at UC Berkeley

betalstm

cada

This is a github repository for Adversarial transfer learning with constrastive coding for time series regression problem.

cnn-convlstm-time-series

Inspired by the success and computational efficiency of convolutional architectures for various sequential tasks compared to recurrent neural networks. We explored CNN and RCNN autoencoder whose representations can be utilized for the task of time-series classification. Our results surpass existing RNN and DTW-based-classifiers on 11 out of 30 datasets while the existing RNN achieved 8/30.

cnn-lstm-bilstm-deepcnn-clstm-in-pytorch

In PyTorch Learing Neural Networks Likes CNN(Convolutional Neural Networks for Sentence Classification (Y.Kim, EMNLP 2014) 、LSTM、BiLSTM、DeepCNN 、CLSTM、CNN and LSTM

cocoapi

COCO API - Dataset @ http://cocodataset.org/

code

competition-baseline

数据科学竞赛知识、代码、思路

contractive-autoencoder

Hands on Contractive Autoencoder

contractive_autoencoders

Contractive auto-encoders implementation in Tensorflow

convlstm_pytorch

Implementation of Convolutional LSTM in PyTorch.

convlstmgru

Pytorch implementations of ConvLSTM and ConvGRU modules with examples

convrnn_for_rul_estimation

Code used in Thesis "Convolutional Recurrent Neural Networks for Remaining Useful Life Prediction in Mechanical Systems".

d2l-zh

《动手学深度学习》：面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。

deep-learning-in-phm

Deep learning in PHM，Deep learning in fault diagnosis，Deep learning in remaining useful life prediction

deep-learning-models

This repository consists of application of Deep Learning Models like DNN, CNN (1D and 2D), RNN (LSTM and GRU) and Variational Autoencoders written from scratch in tensorflow.