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2018-cs109a

Repository for CS109A Fall 2018

2019-cs109a

https://harvard-iacs.github.io/2019-CS109A/

2019-cs109b

a3c_continuous

A continuous action space version of A3C LSTM in pytorch plus A3G design

am205_examples

Harvard Applied Math 205: Code Examples

backprop-neat-js

Neural Network Evolution Playground with Backprop NEAT

cs207-f18

CS207 Repository for Fall 2018

cs234-reinforcement-learning-winter-2019

My Solutions of Assignments of CS234: Reinforcement Learning Winter 2019

deep-neuroevolution

Deep Neuroevolution

deepul

estool

Evolution Strategies Tool

kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

lbfgs_cpp

lbfgs_in_c

lowlevelparticlefilters.jl

Simple particle/kalman filtering, smoothing and parameter estimation

lstmvis

clone of LSTVM

m4_forecasting_fork

models

Models and examples built with TensorFlow

neataptic

:rocket: Blazing fast neuro-evolution & backpropagation for the browser and Node.js

notes

Course notes

openai_es

optimization_algorithms

particles

Sequential Monte Carlo in python

pytorch_notebooks

tutorial notebooks

quantresearch

Quantitative analysis, strategies and backtests

rl_a3c_pytorch

A3C LSTM Atari with Pytorch plus A3G design

smc2017

Collected code and materials from the intensive course preparing for the workshop on Sequential Monte Carlo (SMC) methods at Uppsala University, August 2017

stanford-cs234-rl

The course notes about Stanford CS234 Reinforcement Learning Winter 2019

uber_deep_neuroevolution

yahoo_quote_download

This code downloads end of day stock quote from Yahoo finance.