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getting-started-with-tensorflow

Getting Started with TensorFlow, published by Packt

gpinv

An inverse-problem solver with Gaussian Process priors

gpsmachinelearning

graph-pde

Using graph network to solve PDEs

grv-app-deploy

Repo to deploy the grv-app of the Copenhagen Hackathon to Amazon EC2

gwu_data_mining

Materials for GWU DNSC 6279 and DNSC 6290.

hadoop-particle-swarm-optimization

Map Reduce based Stochastic Particle Swarm Optimization

hands-on-exploratory-data-analysis-with-r

Hands-On-Exploratory-Data-Analysis-with-R, published by Packt

hands-on-image-processing-with-python

hands-on-meta-learning-with-python

Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

hello-world

high_performance_python

Code for the book "High Performance Python" by Micha Gorelick and Ian Ozsvald with OReilly

historymatching

Tutorial on history matching with ensembles

homework

Assignments for CS294-112.

hybrid-images

Combine the high frequency of one image and low frequency of other image to get the hybrid image.

imcmcrun

IMCMCrun is used to perform a seismic inversion using a fast eikonal solver and interactive markov chains algorithm. TODO add reference to the article when it is done. Author : Alexis Bottero (alexis Dot bottero At gmail Dot com). Feel free to contact me for any questions. Source code (c/c++/fortran) can be found in directory src. This program include wavelet library : Wavelib. This program can be run in parallel on a CPU cluster. Directory utils contains some useful python script to watch the results of an inversion.

improved-gan

code for the paper "Improved Techniques for Training GANs"

info8010-deep-learning

Lectures for INFO8010 - Deep Learning, ULiège

introduction-to-data-mining-codes-in-python

Python Code to accompany the book Introduction to Data Mining by Tan, Steinbach and Kumar

introduction_to_data_mining_r_examples

R Code to accompany the book Introduction to Data Mining by Tan, Steinbach and Kumar

ipyparallel

Interactive Parallel Computing in Python

jittquestions

Warm up questions for statistics courses

jstraining

全栈工程师培训材料

kausthack_bootcamp

Material for the Day1 Bootcamp at KAUST Hackathon in Geoscience - March 5th-7th 2022

keras-cloud-ml-engine

Adventures using keras on Google's Cloud ML Engine

keras-complex

Implementation related to the Deep Complex Networks

l2l

Learning to Learn: Gradient-free Optimization framework

lab

A customisable 3D platform for agent-based AI research

latihanvspsu

VSP (Borehole Seismic) Modeling & Processing Exercise using Seismic Unix