soph-loaf's starred repositories
cs231n.github.io
Public facing notes page
stable-baselines3
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
deep-reinforcement-learning
Repo for the Deep Reinforcement Learning Nanodegree program
stable-baselines
A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
higgsfield
Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters
RL-Adventure
Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL
earthengine-api
Python and JavaScript bindings for calling the Earth Engine API.
rl-baselines3-zoo
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
mpc.pytorch
A fast and differentiable model predictive control (MPC) solver for PyTorch.
rl-tutorial-jnrr19
Stable-Baselines tutorial for Journées Nationales de la Recherche en Robotique 2019
num-methods-for-PDEs
Lecture material on numerical methods for partial differential equations.
Numerical-Analysis-Python
Python notebooks for Numerical Analysis
dolfinx-tutorial
A reimplementation of the Springer book: https://github.com/hplgit/fenics-tutorial/, covering new topics as well as transitioning from dolfin to dolfinx
defelement.com
an encyclopedia of finite element definitions
mae-207-fea-for-coupled-problems
Code examples for the class "MAE 207: FEA for coupled problems" at UC San Diego
regulargrid
Regular Grid Multivariate linear interpolation
adios4dolfinx
Extending DOLFINx with checkpointing functionality
intro-numerical-methods
Jupyter notebooks and other materials developed for the Columbia course APMA 4300
uli-workshop-examples-2022
Examples of using the FEniCS finite element software, for the ULI software workshop on 4 February, 2022.
mecs6616_sp23_project4_viz
Viz tools for Columba MECS6616 Project 4 (Robot Learning)
aht_project
Analysis of weather and temperature data to determine heat transfer variation in urban areas as a function of distance to vegetation.