Stars
Long list of geospatial tools and resources
🌐 List & Map of 700+ companies for geospatial jobs (GIS, Earth Observation, UAV, Satellite, Digital Farming, ..)
A curated list of the most important and useful resources about elasticsearch: articles, videos, blogs, tips and tricks, use cases. All about Elasticsearch!
NeuSpell: A Neural Spelling Correction Toolkit
Awesome Search - this is all about the (e-commerce, but not only) search and its awesomeness
An introduction to data science in Python, for people with no programming experience.
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
📋 Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.
A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.
Time-series Generative Adversarial Networks (fork from the ML-AIM research group on bitbucket))
Basic LRP implementation in PyTorch
Jupyter Notebooks and code for Python for Finance (2nd ed., O'Reilly) by Yves Hilpisch.
Codebase for Time-series Generative Adversarial Networks (TimeGAN) - NeurIPS 2019
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
Code for SuDoRm-Rf networks for efficient audio source separation. SuDoRm-Rf stands for SUccessive DOwnsampling and Resampling of Multi-Resolution Features which enables a more efficient way of sep…
Jupyter Notebooks and code for Derivatives Analytics with Python (Wiley Finance) by Yves Hilpisch.
Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
Code repository for O'Reilly book
Python Data Science Handbook: full text in Jupyter Notebooks
Code repo for the book "Feature Engineering for Machine Learning," by Alice Zheng and Amanda Casari, O'Reilly 2018
Code and Resources for "Feature Engineering and Selection: A Practical Approach for Predictive Models" by Kuhn and Johnson
Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
ICCV2019 - Learning to Paint With Model-based Deep Reinforcement Learning
To Trust Or Not To Trust A Classifier. A measure of uncertainty for any trained (possibly black-box) classifier which is more effective than the classifier's own implied confidence (e.g. softmax pr…