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Roadmap to becoming an Artificial Intelligence Expert in 2020
Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier
A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
SCEC Broadband Platform
Code examples in pyTorch and Tensorflow for CS230
Python CPU and GPU accelerated TDEs, over 100 million TDEs per second!
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 175 universities.
IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by J. Nathan Kutz and Steven L. Brunton
Matlab files with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz http://www.databookuw.com/
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
neural networks to learn Koopman eigenfunctions
Official PyTorch implementation of "Deep State Space Models for Nonlinear System Identification", 2020.
A library for scientific machine learning and physics-informed learning
Methods and code for J. L. Callaham, J. N. Kutz, B. W. Brunton, and S. L. Brunton (2020)
Physics-informed learning of governing equations from scarce data
Earthquake Responses prediction using Deep learning and Database
Finite difference code for dynamic modelling of earthquake source.
Python Kalman filtering and optimal estimation library. Implements Kalman filter, particle filter, Extended Kalman filter, Unscented Kalman filter, g-h (alpha-beta), least squares, H Infinity, smoothers, and more. Has companion book 'Kalman and Bayesian Filters in Python'.
A collection of ground motion model functions
GMPE-estimation implements a one-stage estimation algorithm to estimate ground-motion prediction equations (GMPE) with spatial correlation. It also quantifies the uncertainty of spatial correlation and intensity measure predictions.
Kalman filter to combine GPS and accelerometer data
Training, testing, evaluation codes for learning aftershock location patterns
Linear multi time-window earthquake slip inversion with k^-2 smoothing
Machine learning as seismic velocity-model building method for full-waveform inversion
Kinematic and static rupture forward modeling and inversion code
Jupyter notebook with Pytorch implementation of Neural Ordinary Differential Equations
A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.