autoencoder

Build your neural network easy and fast, 莫烦Python中文教学

A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)

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.

[CVPR2020] Adversarial Latent Autoencoders

텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다

Advanced Deep Learning with Keras, published by Packt

Use unsupervised and supervised learning to predict stocks

[unmaintained] An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy

Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano

Next RecSys Library

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

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convol…

TensorFlow and Deep Learning Tutorials

[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.

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

Tensorflow implementation of variational auto-encoder for MNIST

[ICCV 2021] Focal Frequency Loss for Image Reconstruction and Synthesis

Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN