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

Contains all course modules, exercises and notes of ML Specialization by Andrew Ng, Stanford Un. and DeepLearning.ai in Coursera

Source code of CHAMELEON - A Deep Learning Meta-Architecture for News Recommender Systems

e3d-lstm; Eidetic 3D LSTM A Model for Video Prediction and Beyond

Chinese Poetry Generation

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

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 |

AI Exercise Rep Counter based on Google's Human Pose Estimation Library (Posenet)

Recurrent Neural Network (LSTM) by using TensorFlow and Keras in Python for BitCoin price prediction

Char-level RNN LSTM password cracker 🔑🔓.

The implementation of LSTM in TensorFlow used for the stock prediction.

:smile: 从此爸妈没烦恼！！！

Deep Learning neural network for correcting spelling

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.

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!

A deep learning project for automated chorus detection in songs, featuring a command-line interface (CLI) tool that allows users to input a YouTube link and utilize a pre-trained CRNN model to detect chorus sections from a song on YouTube

Implementation of the methods proposed in **Adversarial Training Methods for Semi-Supervised Text Classification** on IMDB dataset (without pre-training)

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.

Implementation of a series of Neural Network architectures in TensorFow 2.0