sischei

DeepEquilibriumNets

global_solution_yale19

crest_comp_econ

awesome-generative-ai-guide

A one stop repository for generative AI research updates, interview resources, notebooks and much more!

ClimateBench

d2l-en

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.

machine-learning-for-trading

Code for Machine Learning for Algorithmic Trading, 2nd edition.

python-training

Python training for business analysts and traders

udlbook

Understanding Deep Learning - Simon J.D. Prince

14_462

Repo for 14_462 Adv. Macroeconomics at MIT

cs-552-modern-nlp

Repository for https://nlp.epfl.ch/cs-552-modern-nlp/

deep-learning-with-python-notebooks

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

Deep-Learning-with-TensorFlow-2-and-Keras

Deep Learning with TensorFlow 2 and Keras, published by Packt

DSE2023

Lectures and conference materials for the DSE2023 at the University of Lausanne, Switzerland

dse_workshop

DSE Conference DP Workshop 2022

ensemble-methods-notebooks

A collection of companion Jupyter notebooks for Ensemble Methods for Machine Learning (Manning, 2020)

financial-machine-learning

A curated list of practical financial machine learning tools and applications.

Financial-Models-Numerical-Methods

Collection of notebooks about quantitative finance, with interactive python code.

fvGP

A software package for flexible HPC GPs

HPC-Tutorials

Future home of hpc-tutorials.llnl.gov

international_trade

jaxton

100 exercises to learn JAX

mesmer

spatially-resolved ESM-specific multi-scenario initial-condition ensemble emulator

ML-YouTube-Courses

📺 Discover the latest machine learning / AI courses on YouTube.

ModellingClimateUncertainty

This is the course material for the Clifirium 2023 conference at the Banque de France

Notebooks

Jupyter notebooks authored by Richard Evans

OptionPricing

ose-course-scientific-computing

course on the basics of scientific computing for economists

pyprobml

Python code for "Probabilistic Machine learning" book by Kevin Murphy

t81_558_deep_learning

Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks