232 Open source projects that are alternatives of or similar to Mss_pytorch

深度学习与PyTorch入门实战视频教程 配套源代码和PPT

Get started with Machine Learning in TensorFlow with a selection of good reads and implemented examples!

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

Semi-Supervised Video Object Segmentation (VOS) with Tensorflow. Includes implementation of *MaskRNN: Instance Level Video Object Segmentation (NIPS 2017)* as part of the NIPS Paper Implementation Challenge.

Variational Autoencoder with Recurrent Neural Network based on Google DeepMind's "DRAW: A Recurrent Neural Network For Image Generation"

videos, lectures, blogs for Deep Learning

Text classifier for Hierarchical Attention Networks for Document Classification

Lyrics Generator aka Character-level Language Modeling with Multi-layer LSTM Recurrent Neural Network

Official PyTorch implementation of paper "A Hybrid Compact Neural Architecture for Visual Place Recognition" by M. Chancán (RA-L & ICRA 2020) https://doi.org/10.1109/LRA.2020.2967324

Text Classification Algorithms: A Survey

parsing scene images with understanding geometric perspective in the loop

Keras implementation of Legendre Memory Units

THEANO-KALDI-RNNs is a project implementing various Recurrent Neural Networks (RNNs) for RNN-HMM speech recognition. The Theano Code is coupled with the Kaldi decoder.

Biologically-realistic recurrent convolutional neural networks

A Recurrent Neural Network to predict Bitcoin value

ECG classification programs based on ML/DL methods

This is just a collection of projects that made during my DEEPLEARNING NANODEGREE by UDACITY

SimpleDNN is a machine learning lightweight open-source library written in Kotlin designed to support relevant neural network architectures in natural language processing tasks

PyTorch Implementation of the RDPG (Recurrent Deterministic Policy Gradient)

This is the end-to-end Speech Recognition neural network, deployed in Keras. This was my final project for Artificial Intelligence Nanodegree @Udacity.

Named Entity Recognition with RNN, implemented by Chainer

Some of the ML and DL related reading materials, research papers that I've read

💻 Make machines learn so that you don't have to struggle to program them; The ultimate list

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)

Visualizing RNNs using the attention mechanism

Implementation of Simple Recurrent Unit in Keras

Machine Learning on Sequential Data Using a Recurrent Weighted Average

Deep Learning based Automatic Speech Recognition with attention for the Nvidia Jetson.

Deep Reinforcement Learning based Trading Agent for Bitcoin

[Deprecated] Source Code Generation using Sequence Generative Adversarial Networks

VIP cheatsheets for Stanford's CS 230 Deep Learning

Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks (ICLR 2018)

Connectionist Temporal Classification (CTC) decoding algorithms: best path, prefix search, beam search and token passing. Implemented in Python.

The source of the IJCAI2017 paper "Modeling Trajectory with Recurrent Neural Networks"

A set of functions for supervised feature learning/classification of mental states from EEG based on "EEG images" idea.

[DEPRECATED] A Neural Network based generative model for captioning images using Tensorflow

Sequence prediction using recurrent neural networks(LSTM) with TensorFlow

3D reconstruction with neural networks using Tensorflow. See link for Video (https://www.youtube.com/watch?v=iI6ZMST8Ri0)

Keras, PyTorch, and NumPy Implementations of Deep Learning Architectures for NLP

Top 200 deep learning Github repositories sorted by the number of stars.

Implementation of State-of-the-art Text Classification Models in Pytorch

Recurrent and multi-process PyTorch implementation of deep reinforcement Actor-Critic algorithms A2C and PPO

RMDL: Random Multimodel Deep Learning for Classification

How to build RNNs and LSTMs from scratch with NumPy.

Anomaly detection implemented in Keras

Deep-Plant: Plant Classification with CNN/RNN. It consists of CAFFE/Tensorflow implementation of our PR-17, TIP-18 (HGO-CNN & PlantStructNet) and MalayaKew dataset.

Komputation is a neural network framework for the Java Virtual Machine written in Kotlin and CUDA C.

Code for the paper: Benhenda, M. 2017. ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity? arXiv preprint arXiv:1708.08227.

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.

A Python package for gender classification.

Simple and comprehensive tutorials in TensorFlow

A bidirectional LSTM with attention for multiclass/multilabel text classification.

Official implementation for NIPS'17 paper: PredRNN: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal LSTMs.

SRU is a recurrent unit that can run over 10 times faster than cuDNN LSTM, without loss of accuracy tested on many tasks.

Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras

Layer-wise Relevance Propagation (LRP) for LSTMs

Plain Stock Close-Price Prediction via Graves LSTM RNNs

Handwriting Synthesis with RNNs ✏️

Handwritten Text Recognition (HTR) system implemented with TensorFlow.