676 Open source projects that are alternatives of or similar to Lstm Autoencoders

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 Convolutional Network.

R, Python and Mathematica Codes in Machine Learning, Deep Learning, Artificial Intelligence, NLP and Geolocation

Variational Recurrent Autoencoder for timeseries clustering in pytorch

Use unsupervised and supervised learning to predict stocks

Keras implementation of video classifier

Codebase for the paper LSTM Fully Convolutional Networks for Time Series Classification

Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.

An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana

Video lstm auto encoder built with pytorch. https://arxiv.org/pdf/1502.04681.pdf

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

Library of autoencoders for sequential data

Twitter's Anomaly Detection in Pure Python

TensorFlow 1.3 experiment with LSTM (and GRU) RNNs for sine prediction

Hands-on in-person workshop for Deep Learning with TensorFlow

Time series based anomaly detector

Java implementation of SAX, HOT-SAX, and EMMA

PyTorch Language Model for 1-Billion Word (LM1B / GBW) Dataset

All my experiments with AI and ML

Time series prediction with Sequential Model and LSTM units

A Pytorch implementation for the ZeroSpeech 2019 challenge.

The deeplearning algorithms implemented by tensorflow

GridDB is a next-generation open source database that makes time series IoT and big data fast,and easy.

Sarcasm detection on tweets using neural network

Char-level RNN LSTM text generator📄.

Anomaly detection algorithm implementation in Python

[IMC 2020 (Best Paper Finalist)] Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions

Tensorflow implementation : U-net and FCN with global convolution

Machine learning, in numpy

Using Deep Autoencoders for predictions of movie ratings.

A python library for time-series smoothing and outlier detection in a vectorized way.

A tutorial on how to implement models for part-of-speech tagging using PyTorch and TorchText.

Administration panel and querying interface for InfluxDB databases. (Electron app / Docker container)

A Tensorflow based implicit recommender system

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

Plot in the terminal using braille dots.

Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier

PyTorch tutorial for learners

Models for predicting emotions from English tweets.

This repo includes the source code of the fully convolutional depth denoising model presented in https://arxiv.org/pdf/1909.01193.pdf (ICCV19)

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.

Deep Learning sample programs using PyTorch in C++

Anything can happen in the next half hour (including spectral timing made easy)!

LSTM and GRU in PyTorch

PyTorch implementations of LSTM Variants (Dropout + Layer Norm)

A fast LSTM Language Model for large vocabulary language like Japanese and Chinese

Library which contains several time-dependent data and index structures (e.g., IntervalTree, BucketTimeSeries), as well as algorithms.

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

Multitask Deep Learning for Sentiment Analysis using Character-Level Language Model, Bi-LSTMs for POS Tag, Chunking and Unsupervised Dependency Parsing. Inspired by this great article https://arxiv.org/abs/1611.01587

Multi-layer Recurrent Neural Networks (LSTM, RNN) for word-level language models in Python using TensorFlow.

Annotation of ECG signals using deep learning, tensorflow’ Keras

RNN and general weights, gradients, & activations visualization in Keras & TensorFlow

CNN, LSTM, NBOW, fasttext 中文文本分类

LSTM and CNN sentiment analysis

Structural Deep Clustering Network

Tutorial for End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

SAX-VSM public release, visit our website for detail

GANs for time series generation in pytorch

Autoencoders in Keras

Handle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.

Graphite InfluxDB backend. InfluxDB storage finder / plugin for Graphite API.