salvador-flores-cl Goto Github PK

bcq

Author's PyTorch implementation of BCQ for continuous and discrete actions

bonsai-cstr

Project Bonsai Sample for Continuous Stirred Tank Reactor (CSTR)

cd4ml-scenarios

Repository with sample code and instructions for "Continuous Intelligence" and "Continuous Delivery for Machine Learning: CD4ML" workshops

d4rl

A benchmark for offline reinforcement learning.

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deep_learning_and_the_game_of_go

Code and other material for the book "Deep Learning and the Game of Go"

dm_control

DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.

imv_lstm

Pytorch implementation of "Exploring Interpretable LSTM Neural Networks over Multi-Variable Data" https://arxiv.org/pdf/1905.12034.pdf

introtodeeplearning

Lab Materials for MIT 6.S191: Introduction to Deep Learning

mastering-reinforcement-learning-with-python

Mastering Reinforcement Learning with Python, published by Packt

microsoft-bonsai-api

A collection of libraries for interfacing simulators with the Bonsai platform.

mlflow-export-import

Export and import MLflow experiments, runs or registered models

mlops-on-gcp

mv-lstm

Multi-variable LSTM recurrent neural networks for prediction and interpretation of multi-variable time series

parasol

prog_algs

The Prognostic Algorithm Package is a python framework for model-based prognostics (computation of remaining useful life) of engineering systems, and provides a set of algorithms for state estimation and prediction, including uncertainty propagation. The algorithms take as inputs prognostic models (from NASA's Prognostics Model Package), and perform estimation and prediction functions. The library allows the rapid development of prognostics solutions for given models of components and systems. Different algorithms can be easily swapped to do comparative studies and evaluations of different algorithms to select the best for the application at hand.

rlkit

Collection of reinforcement learning algorithms

slknn

Papers and code related to 'Less Than One'-Shot (LO-Shot) Learning

turbofan-remaining-life-prediction

Remaining Useful Life (RUL) prediction for Turbofan Engines