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educational

ensai

estimating parameters of strong gravitational lenses with convolutional neural networks

enterprise-network-programmability

ethereumbook

Mastering Ethereum, by Andreas M. Antonopoulos, Gavin Wood

examples

Examples of exercises that accompany Effective Computation in Physics

explorations-in-data-science

feyndyn

Type an M x M matrix for your open quantum system Hamiltonian, and give a spectral density (analytic or numerical). FeynDyn gives the density matrix dynamics according to the Leggett-Caldeira bath or the Feynman-Vernon bath at any temperature. Can do up to 16 qubits (65536 levels) and infinitely many bath modes. Email [email protected] for the latest version or any questions.

fold

Deep learning with dynamic computation graphs in TensorFlow

general-relativity

google-research

Google Research

grove

Quantum algorithms built using pyQuil.

guide-to-qc-and-qi

This guide is for researchers entering the area of Quantum Computation and Quantum Information Science.

hackathon2020

CDL Quantum Hackathon 2020

hands-on-data-science-and-python-machine-learning

Hands-On Data Science and Python Machine Learning, published by Packt

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

ibmq-device-information

Information about the different remote backends available for qiskit users with a IBMQ account

jetson-inference

Guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.

jetsontfbuild

Assistance script to build TensorFlow on an NVIDIA Jetson Module

machine-learning

Machine Learning with Matlab-Octave

mathematica_tensor_examples-1

Example xAct notebooks with common usage patterns

maxcut

QC

may4_challenge_exercises

Original versions of the exercises

mit-deep-learning

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

ml-for-beginners

12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

ml-mock-data-challenge-1

Code and documentation for the first machine learning focused mock data challenge hosted by the Albert-Einstein-Institut Hannover and the Friedrich-Schiller Universität Jena.

mlbookcamp-code

The code from the Machine Learning Bookcamp book and a free course based on the book

models

Models and examples built with TensorFlow

mps-in-julia-minicourse

MPS in Julia minicourse

neo-python

Python Node and SDK for the NEO blockchain

ntl-asteroid-data-hunter

NASA's Asteroid Data Hunter challenge tasked competitors to develop significantly improved algorithms to identify asteroids in images from ground-based telescopes. This winning solution increased the detection sensitivity, minimize the number of false positives, ignore imperfections in the data, and run effectively on all computers.