rnn-tensorflow
There are 614 repositories under rnn-tensorflow topic.
lilianweng/stock-rnn
Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.
hunkim/word-rnn-tensorflow
Multi-layer Recurrent Neural Networks (LSTM, RNN) for word-level language models in Python using TensorFlow.
gy910210/rnn-from-scratch
Implementing Recurrent Neural Network from Scratch
crazydonkey200/tensorflow-char-rnn
Char-RNN implemented using TensorFlow.
sjchoi86/advanced-tensorflow
Little More Advanced TensorFlow Implementations
azminewasi/Machine-Learning-AndrewNg-DeepLearning.AI
Contains all course modules, exercises and notes of ML Specialization by Andrew Ng, Stanford Un. and DeepLearning.ai in Coursera
gabrielspmoreira/chameleon_recsys
Source code of CHAMELEON - A Deep Learning Meta-Architecture for News Recommender Systems
google/e3d_lstm
e3d-lstm; Eidetic 3D LSTM A Model for Video Prediction and Beyond
Disiok/poetry-seq2seq
Chinese Poetry Generation
AFAgarap/gru-svm
[ICMLC 2018] A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection
PacktPublishing/Hands-On-Deep-Learning-Algorithms-with-Python
Hands-On Deep Learning Algorithms with Python, By Packt
gsurma/text_predictor
Char-level RNN LSTM text generator📄.
pvlachas/RNN-RC-Chaos
RNN architectures trained with Backpropagation and Reservoir Computing (RC) methods for forecasting high-dimensional chaotic dynamical systems.
girishp92/Human-activity-recognition-using-Recurrent-Neural-Nets-RNN-LSTM-and-Tensorflow-on-Smartphones
This was my Master's project where i was involved using a dataset from Wireless Sensor Data Mining Lab (WISDM) to build a machine learning model to predict basic human activities using a smartphone accelerometer, Using Tensorflow framework, recurrent neural nets and multiple stacks of Long-short-term memory units(LSTM) for building a deep network. After the model was trained, it was saved and exported to an android application and the predictions were made using the model and the interface to speak out the results using text-to-speech API.
prakashjayy/medium_blogs
medium blog supplementaries | Backprop | Resnet & ResNext | RNN |
AvinashNath2/Recurrent-Neural-Network-for-BitCoin-price-prediction
Recurrent Neural Network (LSTM) by using TensorFlow and Keras in Python for BitCoin price prediction
abishekvashok/Rep-Counter
AI Exercise Rep Counter based on Google's Human Pose Estimation Library (Posenet)
lucko515/tesla-stocks-prediction
The implementation of LSTM in TensorFlow used for the stock prediction.
gsurma/password_cracker
Char-level RNN LSTM password cracker 🔑🔓.
applenob/pick_a_name
:smile: 从此爸妈没烦恼！！！
mdcramer/Deep-Speeling
Deep Learning neural network for correcting spelling
Nazanin1369/stockPredictor
Predict stock movement with Machine Learning and Deep Learning algorithms
mounalab/LSTM-RNN-VAD
Voice Activity Detection LSTM-RNN learning model
aparajitad60/Stacked-LSTM-for-Covid-19-Outbreak-Prediction
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was first identified in December 2019 in Wuhan, China, and has since spread globally, resulting in an ongoing pandemic. Long Short Term Memories(LSTMs) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDS's (intrusion detection systems). LSTMs can also be efficiently applied for time-series predictions. In this project, its shows a four stacked LSTM network for early prediction new Coronavirus dissease infections in some of the mentioned affected countries (India, USA, Czech Republic and Russia) , which is based on real world data sets which are analyzed using various perspectives like day-wise number of confirmed cases, number of Cured cases, death cases. This attempt has been done to help the concerned authorities to get some early insights into the probable devastation likely to be effected by the deadly pandemic.
t04glovern/stylegan-pokemon
Generating Pokemon cards using a mixture of StyleGAN and RNN to create beautiful & vibrant cards ready for battle!
enricivi/adversarial_training_methods
Implementation of the methods proposed in **Adversarial Training Methods for Semi-Supervised Text Classification** on IMDB dataset (without pre-training)
laventura/Music.Generation.with.DeepLearning
Generating Music and Lyrics using Deep Learning via Long Short-Term Recurrent Networks (LSTMs). Implements a Char-RNN in Python using TensorFlow.
manuwhs/BayesianRNN
Reproducing the results of the paper "Bayesian Recurrent Neural Networks" by Fortunato et al.
IvanBongiorni/TensorFlow2.0_Notebooks
Implementation of a series of Neural Network architectures in TensorFow 2.0
zotroneneis/tensorflow_deep_learning_models
TensorFlow implementations of several deep learning models (e.g. variational autoencoder, RNN, ...)
fahd09/ArabicRNN
The first AI-based Arabic songwriter.
nursnaaz/Deeplearning-and-NLP
Deep Learning notes and practical implementation with Tensorflow and keras. Text Analytics and practical application implementation with NLTK, Spacy and Gensim.
angeligareta/image-captioning
Image Caption Generator implemented using Tensorflow and Keras in a Python Jupyter Notebook. The goal is to describe the content of an image by using a CNN and RNN.
tomtom94/stockmarketpredictions
Educational predictions on stock market with Tensorflow.js sequential RNN with LSTM layers on a React web App.
FarshidNooshi/TensorFlow-Notebooks
A collection of notebooks with TensorFlow and the Keras API for various deep-learning and machine learning problems
luiscib3r/Solar-Rad-Forecasting
In 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.