398 Open source projects that are alternatives of or similar to Pointer Networks

All my experiments with AI and ML

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

LSTM and CNN sentiment analysis

Python framework for Speech and Music Detection using Keras.

Image Captioning: Implementing the Neural Image Caption Generator with python

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

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

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

LSTM-CRF for NER with ConLL-2002 dataset

document classification using LSTM + self attention

Tensorflow Implementation of Recurrent Neural Network (Vanilla, LSTM, GRU) for Text Classification

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

1st Place Solution for Zhihu Machine Learning Challenge . Implementation of various text-classification models.(知乎看山杯第一名解决方案)

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

A Natural Language Toolkit for Indian Languages

A PyTorch Tutorials of Sentiment Analysis Classification (RNN, LSTM, Bi-LSTM, LSTM+Attention, CNN)

TensorFlow implementation of multi-layer recurrent neural networks for training and sampling from texts

Models for predicting emotions from English tweets.

keras를 이용한 딥러닝 기초 학습

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

Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc.

I used in this project a reccurent neural network to generate c code based on a dataset of c files from the linux repository.

Implementing different RNN models (LSTM,GRU) & Convolution models (Conv1D, Conv2D) on a subset of Amazon Reviews data with TensorFlow on Python 3. A sentiment analysis project.

This repository contains PyTorch implementation for the baseline models from the paper Utterance-level Dialogue Understanding: An Empirical Study

使用Theano实现的一些RNN代码，包括最基本的RNN，LSTM，以及部分Attention模型，如论文MLSTM等

Gathers machine learning and Tensorflow deep learning models for NLP problems, 1.13 < Tensorflow < 2.0

Long short-term memory recurrent neural networks for learning peptide and protein sequences to later design new, similar examples.

using rnn (lstm or gru) and ctc to convert line image into text, based on torch7 and warp-ctc

light deep neural network tools box(LSTM,GRU,RNN,CNN,Bi-LSTM,etc)

Time series prediction with Sequential Model and LSTM units

Bangla Machine Translator

NN based lossless compression

Sentiment Analysis with LSTMs in Tensorflow

Annotation of ECG signals using deep learning, tensorflow’ Keras

Implementation of Neural Image Captioning model using Keras with Theano backend

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

speaker diarization system using an LSTM

A Tensorflow based implicit recommender system

Named Entity Recognition with RNN, implemented by Chainer

Bert-classification and bert-dssm implementation with keras.

Convert TensorFlow models to ONNX

LSTM and GRU in PyTorch

Some interesting TensorFlow tutorials for beginners.

Ideal Ratio Mask (IRM) Estimation based Speech Enhancement using LSTM

A simple neural network for python autocompletion

Char-level RNN LSTM text generator📄.

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.

Sign Language Recognition for Deaf People

Stock Price Prediction using Machine Learning Techniques

This package contains an OCR engine - libtesseract and a command line program - tesseract. Tesseract 4 adds a new neural net (LSTM) based OCR engine which is focused on line recognition, but also still supports the legacy Tesseract OCR engine of Tesseract 3 which works by recognizing character patterns. Compatibility with Tesseract 3 is enabled by using the Legacy OCR Engine mode (--oem 0). It also needs traineddata files which support the legacy engine, for example those from the tessdata repository.

Application of machine learning to the Coinbase (GDAX) orderbook

Pytorch LSTM RNN for reinforcement learning to play Atari games from OpenAI Universe. We also use Google Deep Mind's Asynchronous Advantage Actor-Critic (A3C) Algorithm. This is much superior and efficient than DQN and obsoletes it. Can play on many games

Anomaly detection for streaming data using autoencoders

GANs for time series generation in pytorch

Machine learning, in numpy

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