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CS231nMy solutions for Assignments of CS231n: Convolutional Neural Networks for Visual Recognition
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Patient2VecPatient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record
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Dialogue UnderstandingThis repository contains PyTorch implementation for the baseline models from the paper Utterance-level Dialogue Understanding: An Empirical Study
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Lstm Ctc Ocrusing rnn (lstm or gru) and ctc to convert line image into text, based on torch7 and warp-ctc
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Lstm Human Activity RecognitionHuman Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier
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DeepzipNN based lossless compression
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Time-Series-ForecastingRainfall analysis of Maharashtra - Season/Month wise forecasting. Different methods have been used. The main goal of this project is to increase the performance of forecasted results during rainy seasons.
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keras-deep-learningVarious implementations and projects on CNN, RNN, LSTM, GAN, etc
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Document Classifier LstmA bidirectional LSTM with attention for multiclass/multilabel text classification.
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deep-spell-checkrKeras implementation of character-level sequence-to-sequence learning for spelling correction
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Sequence-Models-courseraSequence Models by Andrew Ng on Coursera. Programming Assignments and Quiz Solutions.
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Sign LanguageSign Language Recognition for Deaf People
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lstm harLSTM based human activity recognition using smart phone sensor dataset
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Gdax Orderbook MlApplication of machine learning to the Coinbase (GDAX) orderbook
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ConvLSTM-PyTorchConvLSTM/ConvGRU (Encoder-Decoder) with PyTorch on Moving-MNIST
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LSTM-footballMatchWinnerThis repository contains the code for a conference paper "Predicting the football match winner using LSTM model of Recurrent Neural Networks" that we wrote
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Text-AnalysisExplaining textual analysis tools in Python. Including Preprocessing, Skip Gram (word2vec), and Topic Modelling.
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CascadeThis repo contains code to detect sarcasm from text in discussion forum using deep learning
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DeepLogThis is the realization of core DeepLog
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Char rnn lm zhlanguage model in Chinese,基于Pytorch官方文档实现
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parapredParatope Prediction using Deep Learning
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Gradient-SamplesSamples for TensorFlow binding for .NET by Lost Tech
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Time AttentionImplementation of RNN for Time Series prediction from the paper https://arxiv.org/abs/1704.02971
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Ner blstm CrfLSTM-CRF for NER with ConLL-2002 dataset
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Deep-LearningThis repo provides projects on deep-learning mainly using Tensorflow 2.0
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ChaseAutomatic trading bot (WIP)
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Rnn NotebooksRNN(SimpleRNN, LSTM, GRU) Tensorflow2.0 & Keras Notebooks (Workshop materials)
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ThioThio - a playground for real-time anomaly detection
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Pytorchtext1st 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)
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DeepjazzDeep learning driven jazz generation using Keras & Theano!
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SangitaA Natural Language Toolkit for Indian Languages
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traffic-predictionPredict traffic flow with LSTM. For experimental purposes only, unsupported!
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Char Rnn KerasTensorFlow implementation of multi-layer recurrent neural networks for training and sampling from texts
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Pytorch Seq2seqTutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
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rnn2dCPU and GPU implementations of some 2D RNN layers
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VpilotScripts and tools to easily communicate with DeepGTAV. In the future a self-driving agent will be implemented.
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NcrfppNCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.
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DeeplearningfornlpinpytorchAn IPython Notebook tutorial on deep learning for natural language processing, including structure prediction.
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RnnsharpRNNSharp 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.
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