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aas

Code to accompany Advanced Analytics with Spark from O'Reilly Media

algorithms

A collection of algorithms and data structures

axisem

AxiSEM is a parallel spectral-element method to solve 3D wave propagation in a sphere with axisymmetric or spherically symmetric visco-elastic, acoustic, anisotropic structures.

bert

TensorFlow code and pre-trained models for BERT

code_snippets

customer_segmentation_arvato_financial

deep_learning_notes

Playground for deep learning exercise, NLP and CV

deploying-machine-learning-models

Example Repo for the Udemy Course "Deployment of Machine Learning Models"

deployment-of-machine-learning-models

Deployment of Machine Learning Models

dlwpt-code

Code for the book Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann.

dotvim

this is used for vim pulgin

emacs_config

repository to store the init.el and packages installed

eng-intern-assessment-rails

A Starter repo for Shopify Rails Intern assessments

feature-engineering-for-machine-learning

Code repository for the online course Feature Engineering for Machine Learning

feature-selection-for-machine-learning

Code Repository for the online course Feature Selection for Machine Learning

full_stack_spring_boot_react

Code practice for course Full Stack Spring Boot & React

git-sandbox

homebrew

:beer: The missing package manager for OS X.

learningsparkv2

This is the github repo for Learning Spark: Lightning-Fast Data Analytics [2nd Edition]

python_module_example

an example for build a python module

pytorch_kaggle_moa

Pytorch nn implementation for Kaggle MoA competition

sagemaker-deployment

Code and associated files for the deploying ML models within AWS SageMaker

scipy_con_2019

Tutorial Sessions for SciPy Con 2019

seisflows

seismic inversion and adjoint tomography workflows

sentiment_analysis_rnn

Build a Sentiment Analysis Model based on recurrent neural network and deploy it vis SageMaker

shap_loss

Toy example for SHAP loss values

spark-notes

specfem2d

SPECFEM2D simulates forward and adjoint seismic wave propagation in two-dimensional acoustic, (an)elastic, poroelastic or coupled acoustic-(an)elastic-poroelastic media, with Convolution PML absorbing conditions.

specfem3d

SPECFEM3D_Cartesian simulates acoustic (fluid), elastic (solid), coupled acoustic/elastic, poroelastic or seismic wave propagation in any type of conforming mesh of hexahedra (structured or not). It can for instance model seismic waves propagating in sedimentary basins or any other regional geological model following earthquakes. It can also be used for non destructive testing or for ocean acoustics.

spring-and-hibernate-for-beginners

Source code for the course: Spring and Hibernate for Beginners