791 Open source projects that are alternatives of or similar to SpeakerDiarization_RNN_CNN_LSTM

PyTorch tutorial for learners

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

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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.

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

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GPU/CPU (CUDA) Implementation of "Recurrent Memory Array Structures", Simple RNN, LSTM, Array LSTM..

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

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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.

Sentiment Analysis on the First Republic Party debate in 2016 based on Python,NLTK and ML.

Image Captioning: Implementing the Neural Image Caption Generator with python

Named Entity Recognition with RNN, implemented by Chainer

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

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

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Implementations of machine learning algorithm by Python 3

This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Network algorithm, to predict stock prices.

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

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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

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LSTM based human activity recognition using smart phone sensor dataset

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