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Notes on topics ranging from Recurrent Newtorks to Automatic Control and Reinforcement Learning

License: MIT License

CSS 0.11% JavaScript 0.12% HTML 44.48% Python 0.05% Jupyter Notebook 55.24% Shell 0.01%
machine-learning jupyter-notebook deep-learning data-science lecture-notes python recurrent-neural-networks automatic-control reinforcement-learning dynamic-programming dynamic-systems

control-notes's Introduction

A collection of notebooks on Dynamic Systems, Automatic Control, and Reinforcement Learning

Current topics

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Topics in control and machine learning

Imitation Learning Autoregressive processes Recurrent networks



PID Control



Dynamic Systems

Moving averages 1D convolutions Dynamic systems with limited memory



Markov chains and stochastic recurrence relations Dynamic systems with unlimited memory Recurrence relations




Data preprocessing and feature normalization

ZCA sphereing    




Installation

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To successfully run the Jupyter notebooks contained in this repo we highly recommend downloading the Anaconda Python 3 distribution. Many of these notebooks also employ the Automatic Differentiator autograd which can be installed by typing the following command at your terminal

  pip install autograd

With minor adjustment users can also run these notebooks using the GPU/TPU extended version of autograd JAX.


Creators

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This repository is in active development by Jeremy Watt and Reza Borhani - please do not hesitate to reach out with comments, questions, typos, etc.

control-notes's People

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