There are 13 repositories under rnn-tensorflow topic.
Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.
Multi-layer Recurrent Neural Networks (LSTM, RNN) for word-level language models in Python using TensorFlow.
Implementing Recurrent Neural Network from Scratch
Char-RNN implemented using TensorFlow.
Little More Advanced TensorFlow Implementations
Source code of CHAMELEON - A Deep Learning Meta-Architecture for News Recommender Systems
Contains all course modules, exercises and notes of ML Specialization by Andrew Ng, Stanford Un. and DeepLearning.ai in Coursera
Chinese Poetry Generation
Hands-On Deep Learning Algorithms with Python, By Packt
Char-level RNN LSTM text generator📄.
RNN architectures trained with Backpropagation and Reservoir Computing (RC) methods for forecasting high-dimensional chaotic dynamical systems.
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.
medium blog supplementaries | Backprop | Resnet & ResNext | RNN |
Recurrent Neural Network (LSTM) by using TensorFlow and Keras in Python for BitCoin price prediction
The implementation of LSTM in TensorFlow used for the stock prediction.
AI Exercise Rep Counter based on Google's Human Pose Estimation Library (Posenet)
Char-level RNN LSTM password cracker 🔑🔓.
Deep Learning neural network for correcting spelling
Voice Activity Detection LSTM-RNN learning model
Generating Pokemon cards using a mixture of StyleGAN and RNN to create beautiful & vibrant cards ready for battle!
Predict stock movement with Machine Learning and Deep Learning algorithms
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.
Implementation of the methods proposed in **Adversarial Training Methods for Semi-Supervised Text Classification** on IMDB dataset (without pre-training)
Implementation of a series of Neural Network architectures in TensorFow 2.0
Generating Music and Lyrics using Deep Learning via Long Short-Term Recurrent Networks (LSTMs). Implements a Char-RNN in Python using TensorFlow.
Reproducing the results of the paper "Bayesian Recurrent Neural Networks" by Fortunato et al.
TensorFlow implementations of several deep learning models (e.g. variational autoencoder, RNN, ...)
Deep Learning notes and practical implementation with Tensorflow and keras. Text Analytics and practical application implementation with NLTK, Spacy and Gensim.
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.
A collection of notebooks with TensorFlow and the Keras API for various deep-learning and machine learning problems
Educational predictions on stock market with Tensorflow.js sequential RNN with LSTM layers on a React web App.
TensorFlow implementation of Graphical Attention Recurrent Neural Networks based on work by Cirstea et al., 2019.