GitHub

LSTM and RNN Tutorial with Demo (with Stock/Bitcoin Time Series Prediction, Sentiment Analysis, Music Generation)

What is Deep Learning (DL)? "Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks." There are ...
Hacker

Build an Abstractive Text Summarizer in 94 Lines of Tensorflow !! (Tutorial 6)

This tutorial is the sixth one from a series of tutorials that would help you build an abstractive text summarizer using tensorflow , today we would build an abstractive text summarizer in tensorflow ...
GitHub

char_rnn_classification_tutorial.py

This tutorials is part of a three-part series: * `NLP From Scratch: Classifying Names with a Character-Level RNN <https://pytorch.org/tutorials/intermediate/char_rnn ...
IEEE

A Tutorial on Quantum Graph Recurrent Neural Network (QGRNN)

Abstract: Over the past decades, various neural networks have been proposed with the rapid development of the machine learning field. In particular, graph neural networks using feature-vectors ...
eLife

Evidence from pupillometry, fMRI, and RNN modelling shows that gain neuromodulation mediates task-relevant perceptual switches

Perceptual updating has been hypothesised to rely on a network reset modulated by bursts of ascending neuromodulatory neurotransmitters, such as noradrenaline, abruptly altering the brain’s ...
Frontiers

Exploring Flip Flop memories and beyond: training Recurrent Neural Networks with key insights

Training neural networks to perform different tasks is relevant across various disciplines. In particular, Recurrent Neural Networks (RNNs) are of great interest in Computational Neuroscience.
Scientific Research Publishing

Staudemeyer, R.C. and Morris, E.R. (2019) Understanding LSTM—A Tutorial into Long Short-Term Memory Recurrent Neural Networks.

ABSTRACT: This paper examines the effectiveness of the Differential autoregressive integrated moving average (ARIMA) model in comparison to the Long Short Term Memory (LSTM) neural network model for ...
IEEE

Training Recurrent Neural Network-Based Model to Predict COVID-19 Patient Risk for PASC using Pytorch_EHR – Hands-on Tutorial on N3C

Abstract: Pytorch_EHR is a codebase enabling fast prototyping of deep learning-based predictive models using electronic health records structured data. Rather than a collection of vertical pipelines ...
Scientific Research Publishing

Staudemeyer, R. C., & Morris, E. R. (2019). Understanding LSTM—A Tutorial into Long Short-Term Memory Recurrent Neural Networks.

ABSTRACT: This paper focuses on the use of machine learning models to forecast economic recessions caused by incidents such as the COVID-19 pandemic. Relevant economic variables are selected to fit ...