Shane Keller's starred repositories
the-turing-way
Host repository for The Turing Way: a how to guide for reproducible data science
model-analysis
Model analysis tools for TensorFlow
DS-Take-Home
My solution to the book A Collection of Data Science Take-Home Challenges
Spotify-Song-Recommendation-ML
UC Berkeley team's submission for RecSys Challenge 2018
open_source_demos
A collection of demos showcasing automated feature engineering and machine learning in diverse use cases
Statistical-Rethinking
An interactive online reading of McElreath's Statistical Rethinking
UDACITY-self-driving-car
Udacity Self-Driving Car Engineer Nanodegree projects
data_science_for_all
Code and resources for my blog and articles to share Data Science and AI knowledge and learnings with everyone
Data-Science--Cheat-Sheet
Cheat Sheets
PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
Production-Level-Deep-Learning
A guideline for building practical production-level deep learning systems to be deployed in real world applications.
sklearn-benchmarks
A centralized repository to report scikit-learn model performance across a variety of parameter settings and data sets.
cracking-the-data-science-interview
A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep
machine-learning-systems-design
A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"
cloudml-samples
Cloud ML Engine repo. Please visit the new Vertex AI samples repo at https://github.com/GoogleCloudPlatform/vertex-ai-samples
nlp-recipes
Natural Language Processing Best Practices & Examples
nlp-roadmap
ROADMAP(Mind Map) and KEYWORD for students those who have interest in learning NLP
tech-interview-handbook
💯 Curated coding interview preparation materials for busy software engineers
techniques
Techniques for deep learning with satellite & aerial imagery
awesome-satellite-imagery-datasets
🛰️ List of satellite image training datasets with annotations for computer vision and deep learning