Deep-Learning-CourseraProjects from the Deep Learning Specialization from deeplearning.ai provided by Coursera
Stars: ✭ 123 (-53.41%)
rnn2dCPU and GPU implementations of some 2D RNN layers
Stars: ✭ 26 (-90.15%)
char-VAEInspired by the neural style algorithm in the computer vision field, we propose a high-level language model with the aim of adapting the linguistic style.
Stars: ✭ 18 (-93.18%)
sp2cpImageboard bot with recurrent neural network (RNN, GRU)
Stars: ✭ 23 (-91.29%)
CS231nMy solutions for Assignments of CS231n: Convolutional Neural Networks for Visual Recognition
Stars: ✭ 30 (-88.64%)
novel writerTrain LSTM to writer novel (HongLouMeng here) in Pytorch.
Stars: ✭ 14 (-94.7%)
ScrambleTestsRunning compostionality tests on InferSent embedding on SNLI
Stars: ✭ 16 (-93.94%)
GestureAIRNN(Recurrent Nerural network) model which recognize hand-gestures drawing 5 figures.
Stars: ✭ 20 (-92.42%)
cnn-rnn-classifierA practical example on how to combine both a CNN and a RNN to classify images.
Stars: ✭ 47 (-82.2%)
Speech-RecognitionEnd-to-end Automatic Speech Recognition for Madarian and English in Tensorflow
Stars: ✭ 21 (-92.05%)
altairAssessing Source Code Semantic Similarity with Unsupervised Learning
Stars: ✭ 42 (-84.09%)
theano-recurrenceRecurrent Neural Networks (RNN, GRU, LSTM) and their Bidirectional versions (BiRNN, BiGRU, BiLSTM) for word & character level language modelling in Theano
Stars: ✭ 40 (-84.85%)
Sequence-Models-courseraSequence Models by Andrew Ng on Coursera. Programming Assignments and Quiz Solutions.
Stars: ✭ 53 (-79.92%)
DeepLearning-LabCode lab for deep learning. Including rnn,seq2seq,word2vec,cross entropy,bidirectional rnn,convolution operation,pooling operation,InceptionV3,transfer learning.
Stars: ✭ 83 (-68.56%)
sgrnnTensorflow implementation of Synthetic Gradient for RNN (LSTM)
Stars: ✭ 40 (-84.85%)
air writingOnline Hand Writing Recognition using BLSTM
Stars: ✭ 26 (-90.15%)
deep-improvisationEasy-to-use Deep LSTM Neural Network to generate song sounds like containing improvisation.
Stars: ✭ 53 (-79.92%)
FARED for Anomaly DetectionOfficial source code of "Fast Adaptive RNN Encoder-Decoder for Anomaly Detection in SMD Assembly Machine"
Stars: ✭ 14 (-94.7%)
tiny-rnnLightweight C++11 library for building deep recurrent neural networks
Stars: ✭ 41 (-84.47%)
NoiseReductionUsingGRUThis is my graduation project in BIT. Title: Noise Reduction Using GRU.
Stars: ✭ 25 (-90.53%)
CDRP TFCNN Event Detection & RNN Phase Picking (in Tensorflow)
Stars: ✭ 20 (-92.42%)
Solar-Rad-ForecastingIn these notebooks the entire research and implementation process carried out for the construction of various machine learning models based on neural networks that are capable of predicting levels of solar radiation is captured given a set of historical data taken by meteorological stations.
Stars: ✭ 24 (-90.91%)
time-series-forecasting-tensorflowjsPull stock prices from online API and perform predictions using Long Short Term Memory (LSTM) with TensorFlow.js framework
Stars: ✭ 96 (-63.64%)
ConvLSTM-PyTorchConvLSTM/ConvGRU (Encoder-Decoder) with PyTorch on Moving-MNIST
Stars: ✭ 202 (-23.48%)
hcnHybrid Code Networks https://arxiv.org/abs/1702.03274
Stars: ✭ 81 (-69.32%)
mangoQuestion-Answering NLP model with character-level RNN (TensorFlow).
Stars: ✭ 15 (-94.32%)
yunyi2018“云移杯- 景区口碑评价分值预测
Stars: ✭ 29 (-89.02%)
Dense BiLSTMTensorflow Implementation of Densely Connected Bidirectional LSTM with Applications to Sentence Classification
Stars: ✭ 48 (-81.82%)
ECGClassifierCNN, RNN, and Bayesian NN classification for ECG time-series (using TensorFlow in Swift and Python)
Stars: ✭ 53 (-79.92%)
ML2017FALLMachine Learning (EE 5184) in NTU
Stars: ✭ 66 (-75%)
sequence-rnn-pySequence analyzing using Recurrent Neural Networks (RNN) based on Keras
Stars: ✭ 28 (-89.39%)
PFL-Non-IIDThe origin of the Non-IID phenomenon is the personalization of users, who generate the Non-IID data. With Non-IID (Not Independent and Identically Distributed) issues existing in the federated learning setting, a myriad of approaches has been proposed to crack this hard nut. In contrast, the personalized federated learning may take the advantage…
Stars: ✭ 58 (-78.03%)
SpeakerDiarization RNN CNN LSTMSpeaker 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).
Stars: ✭ 56 (-78.79%)
modulesThe 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.
Stars: ✭ 25 (-90.53%)
Customer-Feedback-AnalysisMulti Class Text (Feedback) Classification using CNN, GRU Network and pre trained Word2Vec embedding, word embeddings on TensorFlow.
Stars: ✭ 18 (-93.18%)
question-pairA siamese LSTM to detect sentence/question pairs.
Stars: ✭ 25 (-90.53%)
Pytorch-POS-TaggerPart-of-Speech Tagger and custom implementations of LSTM, GRU and Vanilla RNN
Stars: ✭ 24 (-90.91%)
solar-forecasting-RNNMulti-time-horizon solar forecasting using recurrent neural network
Stars: ✭ 29 (-89.02%)
GAN-RNN Timeseries-imputationRecurrent GAN for imputation of time series data. Implemented in TensorFlow 2 on Wikipedia Web Traffic Forecast dataset from Kaggle.
Stars: ✭ 107 (-59.47%)
VariationalNeuralAnnealingA variational implementation of classical and quantum annealing using recurrent neural networks for the purpose of solving optimization problems.
Stars: ✭ 21 (-92.05%)
totally humansrnn trained on r/totallynotrobots 🤖
Stars: ✭ 23 (-91.29%)
captioning chainerA fast implementation of Neural Image Caption by Chainer
Stars: ✭ 17 (-93.56%)
ms-convSTAR[RSE21] Pytorch code for hierarchical time series classification with multi-stage convolutional RNN
Stars: ✭ 17 (-93.56%)
myDLDeep Learning
Stars: ✭ 18 (-93.18%)