Popular GitHub Repositories for Autoencoder

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学

This repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code.

Advanced Deep Learning with Keras, published by Packt

Use unsupervised and supervised learning to predict stocks

Next RecSys Library

[NeurIPS 2020] Official code for the paper "DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation". Includes a PyTorch library for deep learning with SVG data.

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series

DanceNet -??Dance generator using Autoencoder, LSTM and Mixture Density Network. (Keras)

Tensorflow implementation of variational auto-encoder for MNIST

Variational Recurrent Autoencoder for timeseries clustering in pytorch

Generative Adversarial Networks and Autoencoders for 3D Shapes

PyTorch implementation of Soft Actor-Critic + Autoencoder(SAC+AE)

KitNET is a lightweight online anomaly detection algorithm, which uses an ensemble of autoencoders.

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

Anomaly detection for streaming data using autoencoders

Visualization techniques for the latent space of a convolutional autoencoder in Keras

Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study

Source code for the paper titled "Speech Denoising without Clean Training Data: a Noise2Noise Approach". Paper accepted at the INTERSPEECH 2021 conference. This paper tackles the problem of the heavy dependence of clean speech data required by deep learning based audio denoising methods by showing that it is possible to train deep speech denoising networks using only noisy speech samples.