torch-lrcnAn implementation of the LRCN in Torch
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Robust-Deep-Learning-PipelineDeep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data. Human Activity Recognition Challenge. Springer SIST (2020)
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MTL-AQAWhat and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment [CVPR 2019]
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pose2actionexperiments on classifying actions using poses
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Video ClassificationTutorial for video classification/ action recognition using 3D CNN/ CNN+RNN on UCF101
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Movienet ToolsTools for movie and video research
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Image Caption GeneratorA neural network to generate captions for an image using CNN and RNN with BEAM Search.
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StockpredictionPlain Stock Close-Price Prediction via Graves LSTM RNNs
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Multilstmkeras attentional bi-LSTM-CRF for Joint NLU (slot-filling and intent detection) with ATIS
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Lstm AutoencodersAnomaly detection for streaming data using autoencoders
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Abstractive SummarizationImplementation of abstractive summarization using LSTM in the encoder-decoder architecture with local attention.
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Handwriting SynthesisImplementation of "Generating Sequences With Recurrent Neural Networks" https://arxiv.org/abs/1308.0850
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Pytorch convlstmconvolutional lstm implementation in pytorch
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TesseractThis 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.
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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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JlmA fast LSTM Language Model for large vocabulary language like Japanese and Chinese
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ContextConText v4: Neural networks for text categorization
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See RnnRNN and general weights, gradients, & activations visualization in Keras & TensorFlow
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Deep GenerationI used in this project a reccurent neural network to generate c code based on a dataset of c files from the linux repository.
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HakeHAKE: Human Activity Knowledge Engine (CVPR'18/19/20, NeurIPS'20)
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Linear Attention Recurrent Neural NetworkA 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)
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Nlp Models TensorflowGathers machine learning and Tensorflow deep learning models for NLP problems, 1.13 < Tensorflow < 2.0
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MmactionAn open-source toolbox for action understanding based on PyTorch
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Rnn Text Classification TfTensorflow Implementation of Recurrent Neural Network (Vanilla, LSTM, GRU) for Text Classification
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EasyocrReady-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
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I3d finetuneTensorFlow code for finetuning I3D model on UCF101.
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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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Chinese Chatbot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。
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Lstms.pthPyTorch implementations of LSTM Variants (Dropout + Layer Norm)
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Lstm CrfA (CNN+)RNN(LSTM/BiLSTM)+CRF model for sequence labelling.😏
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ExermoteUsing Machine Learning to predict the type of exercise from movement data
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Numpy MlMachine learning, in numpy
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Document Classifier LstmA bidirectional LSTM with attention for multiclass/multilabel text classification.
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Awesome Deep Learning ResourcesRough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier
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Daguan 2019 rank9datagrand 2019 information extraction competition rank9
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Repo 2016R, Python and Mathematica Codes in Machine Learning, Deep Learning, Artificial Intelligence, NLP and Geolocation
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Deep Learning With PythonExample projects I completed to understand Deep Learning techniques with Tensorflow. Please note that I do no longer maintain this repository.
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Stock Market Prediction Web App Using Machine Learning And Sentiment AnalysisStock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall
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Pytorch gbw lmPyTorch Language Model for 1-Billion Word (LM1B / GBW) Dataset
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DeeplearningfornlpinpytorchAn IPython Notebook tutorial on deep learning for natural language processing, including structure prediction.
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TddTrajectory-pooled Deep-Convolutional Descriptors
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A RecsysA Tensorflow based implicit recommender system
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Action RecognitionExploration of different solutions to action recognition in video, using neural networks implemented in PyTorch.
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Reinforcementlearning AtarigamePytorch 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
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SarcasmdetectionSarcasm detection on tweets using neural network
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Text predictorChar-level RNN LSTM text generator📄.
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Keras Kinetics I3dkeras implementation of inflated 3d from Quo Vardis paper + weights
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