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18337

18.337 - Parallel Computing and Scientific Machine Learning

aidungeon

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-neural-ode

A collection of resources regarding the interplay between ODEs, dynamical systems and neural networks

bytenet-tensorflow

ByteNet for character-level language modelling

ca-ip3-simulations

Simulations of calcium and IP3 dynamics in Julia.

calciumfittings

canto5

Análisis de señales de canto de pajaros

data-assisted

Code for data-assisted reduced-order modeling of extreme events in complex dynamical systems, available on arXiv: https://arxiv.org/abs/1803.03365

datadrivendiffeq.jl

A simple package for data driven modeling of dynamical systems

deeptimeseries

A Keras-based library for analysis of time series data using deep learning algorithms.

dl-eeg-tutorial

Hands-on tutorial on deep learning for EEG classification.

fittings

flux.jl

Relax! Flux is the ML library that doesn't make you tensor

generativeadversarialnetworks

ho-python

Hands-on de Python

hocompiled

Hands-On de Compiled Languages

hodebug-profile

Hands-On de Debugging y Profiling

hogit

Repositorio con ejercicios para practicar comandos básicos de git

hooop

Hands-On de Object Oriented Programming

how-to-learn-from-little-data

This is the code for "How to Learn from Little Data - Intro to Deep Learning #17' by Siraj Raval on YouTube

idnns

explore DNNs via Infomration

julia

The Julia Language: A fresh approach to technical computing.

julia-for-ds-and-sciml

Codes used for a 1 hour overview of Julia for Dynamical Systems and Scientific Machine Learning, given at the Dynamical Systems Reading Group of Mila.

kaggle-web-traffic

1st place solution

latentdiffeq.jl

Latent Differential Equations models in Julia.

lux.jl

Explicitly Parameterized Neural Networks in Julia

mat6115_dynamical_systems

Support material for MAT6115, Université de Montréal, Fall 2018

mlutils.jl

Utilities and abstractions for Machine Learning tasks

multivariate-time-series-forecast-using-seq2seq-in-tensorflow