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This repo aims to be a useful collection of notebooks/code for understanding and implementing seq2seq neural networks for time series forecasting. Networks are constructed with keras/tensorflow.
A repository containing tutorials for practical NLP using PyTorch
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
Re-implement Kaiming He's deep residual networks in tensorflow. Can be trained with cifar10.
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
Residual networks implementation using Keras-1.0 functional API
The Marine Systems Simulator (MSS) is software that supplements the textbook "Handbook of Marine Craft Hydrodynamics and Motion Control," 2nd Edition, by T. I. Fossen, published in 2021 by John Wil…
Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier
This repository holds all the code for the site http://www.adventuresinmachinelearning.com