6859 Open source projects that are alternatives of or similar to Telemanom

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT.

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.

Anomaly detection for temporal data using LSTMs

A recurrent attention module consisting of an LSTM cell which can query its own past cell states by the means of windowed multi-head attention. The formulas are derived from the BN-LSTM and the Transformer Network. The LARNN cell with attention can be easily used inside a loop on the cell state, just like any other RNN. (LARNN)

RNN based Time-series Anomaly detector model implemented in Pytorch.

RNN(SimpleRNN, LSTM, GRU) Tensorflow2.0 & Keras Notebooks (Workshop materials)

Time series Timeseries Deep Learning Pytorch fastai - State-of-the-art Deep Learning with Time Series and Sequences in Pytorch / fastai

Load forcasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models

List of papers, code and experiments using deep learning for time series forecasting

An open-source, low-code machine learning library in Python

PyTorch tutorial for learners

All about Neural Networks!

Implementation of RNN for Time Series prediction from the paper https://arxiv.org/abs/1704.02971

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

1st place solution

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

Pytorch implementation of "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling" (https://arxiv.org/abs/1609.01454)

Stock Price Prediction using Machine Learning Techniques

Keras tutorial for beginners (using TF backend)

Sequence prediction using recurrent neural networks(LSTM) with TensorFlow

My Attempt(s) In The World Of ML/DL....

Implementation of the paper - Generative Recurrent Networks for De Novo Drug Design.

中文聊天机器人，基于10万组对白训练而成，采用注意力机制，对一般问题都会生成一个有意义的答复。已上传模型，可直接运行，跑不起来直播吃键盘。

All my experiments with AI and ML

A cute multi-layer LSTM that can perform like a human 🎶

Natural Language Processing with TensorFlow, published by Packt

Tensorflow implementation of paper: Learning to Diagnose with LSTM Recurrent Neural Networks.

Tutorials on getting started with PyTorch and TorchText for sentiment analysis.

ConvLSTM/ConvGRU (Encoder-Decoder) with PyTorch on Moving-MNIST

Bitcoin price Prediction ( Time Series ) using LSTM Recurrent neural network

This repository contains Ipython notebooks and datasets for the data analytics youtube tutorials on The Semicolon.

Sentiment Analysis with LSTMs in Tensorflow

Chinese Poetry Generation

Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

Tutorial for video classification/ action recognition using 3D CNN/ CNN+RNN on UCF101

Tensorflow Implementation of Densely Connected Bidirectional LSTM with Applications to Sentence Classification

Sequence Models by Andrew Ng on Coursera. Programming Assignments and Quiz Solutions.

My solutions for Assignments of CS231n: Convolutional Neural Networks for Visual Recognition

Remaining Useful Life (RUL) estimation of Lithium-ion batteries using deep LSTMs

CPU and GPU implementations of some 2D RNN layers

Pull stock prices from online API and perform predictions using Long Short Term Memory (LSTM) with TensorFlow.js framework

PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).

High-performance time series algorithms for Ruby

Lightweight C++11 library for building deep recurrent neural networks

Hybrid Code Networks https://arxiv.org/abs/1702.03274

Tensorflow implementation of Synthetic Gradient for RNN (LSTM)

分别使用rnn/cnn/rcnn来实现根据患者描述，进行疾病诊断

rnn trained on r/totallynotrobots 🤖

Part-of-Speech Tagger and custom implementations of LSTM, GRU and Vanilla RNN

Thio - a playground for real-time anomaly detection

Cryptocurrency Price Prediction Using LSTM neural network

Machine Learning Lectures at the European Space Agency (ESA) in 2018

Tidy anomaly detection

RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above versions. RNNSharp supports many different types of networks, such as forward and bi-directional network, sequence-to-sequence network, and different types of layers, such as LSTM, Softmax, sampled Softmax and others.

Image Captioning using InceptionV3 and beam search

Repository for the tutorial on Sequence-Aware Recommender Systems held at TheWebConf 2019 and ACM RecSys 2018

A collection of UNet and hybrid architectures in PyTorch for 2D and 3D Biomedical Image segmentation

中文古诗自动作诗机器人，屌炸天，基于tensorflow1.10 api，正在积极维护升级中，快star，保持更新！

Easy-to-use Deep LSTM Neural Network to generate song sounds like containing improvisation.