Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier
The main features of guillaume-chevalier/seq2seq-signal-prediction are: Deep Learning Frameworks, Neural Network Architectures, Educational Tutorials.
Open-source alternatives to guillaume-chevalier/seq2seq-signal-prediction include: guillaume-chevalier/lstm-human-activity-recognition — Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the… karpathy/char-rnn — This project is a character-level language modeling system that uses recurrent neural networks to predict and generate… alrojo/tensorflow-tutorial — Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE. aymericdamien/tensorflow-examples — This repository serves as a structured educational resource for machine learning and deep learning, providing a… binroot/tensorflow-book — This project is a collection of TensorFlow machine learning examples providing reference implementations for various… leriomaggio/deep-learning-keras-tensorflow — Introduction to Deep Neural Networks with Keras and Tensorflow.
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier
This project is a collection of TensorFlow machine learning examples providing reference implementations for various neural network paradigms. It covers supervised, unsupervised, reinforcement, and sequential learning models. The repository includes implementations for convolutional neural networks focused on image classification and ranking, as well as recurrent neural networks for time-series forecasting and sequence-to-sequence translation. It further provides examples of reinforcement learning agents trained via reward optimization and unsupervised learning techniques such as autoencoders
Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE
This repository serves as a structured educational resource for machine learning and deep learning, providing a library of executable scripts and notebooks. It is designed to help users master the practical application of data processing, model evaluation, and neural network construction through annotated code samples and guided tutorials. The collection focuses on translating theoretical mathematical concepts into functional code, offering proven patterns for common tasks such as classification and regression. By providing curated examples of layer construction and training loops, the reposi