893 Open source projects that are alternatives of or similar to Codegan

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.

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

Recurrent Neural Network and Long Short Term Memory (LSTM) with Connectionist Temporal Classification implemented in Theano. Includes a Toy training example.

Comparing Fixed and Adaptive Computation Time for Recurrent Neural Networks

Neural networks library for machine learning on PHP

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

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)

Use tensorflow's tf.scan to build vanilla, GRU and LSTM RNNs

A variational implementation of classical and quantum annealing using recurrent neural networks for the purpose of solving optimization problems.

<머신러닝 교과서 with 파이썬, 사이킷런, 텐서플로>의 코드 저장소

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

Char-RNN implemented using TensorFlow.

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

Speaker Diarization is the problem of separating speakers in an audio. There could be any number of speakers and final result should state when speaker starts and ends. In this project, we analyze given audio file with 2 channels and 2 speakers (on separate channels).

Human activity recognition using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six categories (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING).

Python library for Reservoir Computing using Echo State Networks

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

iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press

Computer-generated poetry

The Official Deep Learning Framework for Route-based Place Recognition

Probabilistic Dialogue Act Classification for the Switchboard Corpus using an LSTM model

[AAAI 2020] Modeling Personality with Attentive Networks and Contextual Embeddings

The official repository for our paper "Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks". We develop a method for analyzing emerging functional modularity in neural networks based on differentiable weight masks and use it to point out important issues in current-day neural networks.

Multiple implementations for abstractive text summurization , using google colab

pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.

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.

Deep learning codes and projects using Python

Alternative approach for Adaptive Computation Time in TensorFlow

RMDL: Random Multimodel Deep Learning for Classification

Sequence analyzing using Recurrent Neural Networks (RNN) based on Keras

Automate swing trading using deep reinforcement learning. The deep deterministic policy gradient-based neural network model trains to choose an action to sell, buy, or hold the stocks to maximize the gain in asset value. The paper also acknowledges the need for a system that predicts the trend in stock value to work along with the reinforcement …

End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow

PyTorch tutorial for learners

[ICMLC 2018] A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection

Quantize weights and activations in Recurrent Neural Networks.

Tensorflow implementation of Synthetic Gradient for RNN (LSTM)

Recurrent (conditional) generative adversarial networks for generating real-valued time series data.

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

조별과제 때마다 "나무위키 꺼라."라고 말하시는게 피곤하신 여러분을 위해 만들어진 Browser Extension, 나무위키를 꺼 드립니다.

Reproduce research from paper "Predicting the direction of stock market prices using random forest"

Overview of Modern Deep Learning Techniques Applied to Natural Language Processing

NAACL 2019: Submodular optimization-based diverse paraphrasing and its effectiveness in data augmentation

Snipit allows you to capture and save interesting sections from any source of information. Be it textbooks, journals, computer screens, photographs, flyers, writings on a whiteboard, etc.

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.

A collection of resources on using BERT (https://arxiv.org/abs/1810.04805 ) and related Language Models in production environments.

This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"

Adding RPG to your server has never been more fun and action-packed!

Papers about known hacking, security, hardware, software, computer, network and other ressources.

论文“Attention-over-Attention Neural Networks for Reading Comprehension”中AoA模型实现

Keras Temporal Convolutional Network.

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

Reference Implementation for WSDM 2018 Paper "Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering"

Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"

language model in Chinese，基于Pytorch官方文档实现

Who Let The Dogs Out? Modeling Dog Behavior From Visual Data https://arxiv.org/pdf/1803.10827.pdf

Handwritten Text Recognition (HTR) system implemented with TensorFlow.

Image Captioning: Implementing the Neural Image Caption Generator with python

An extension on the Hierachical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion, our implementation is in Tensorflow and uses an attention mechanism.

📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.