rikazry's repositories
data-science-interviews
Data science interview questions and answers
Whole-Foods-Delivery-Slot
Automated script for Whole Foods and Amazon Fresh delivery slot
awesome-artificial-intelligence-guidelines
This repository aims to map the ecosystem of artificial intelligence guidelines, principles, codes of ethics, standards, regulation and beyond.
awesome-python
A curated list of awesome Python frameworks, libraries, software and resources
CrossSection
Code to accompany our paper Chen and Zimmermann (2020), "Open source cross-sectional asset pricing"
Data_Insights_Summit
This repository contains the course materials for the tutorial "Supercharge Your data Analysis with R"
DeepLearning-500-questions
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
effectivepython
Effective Python: Source Code and Errata for the Book
efficientR
Efficient R programming: a book
go-finance
Flexible, simple financial markets data in Go.
Machine_Learning_Resources
:fish::fish::fish: 机器学习面试复习资源
ml-interviews-book
https://huyenchip.com/ml-interviews-book/
notes-machine-learning
My Notes on Machine Learning
pandas-tutorial
Material for the pandas tutorial at EuroScipy 2016
pandas_exercises
Practice your pandas skills!
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
pydata-book
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
scientific-python-lectures
Lectures on scientific computing with python, as IPython notebooks.
ShinyDeveloperConference
Materials collected from the First Shiny Developer Conference Palo Alto, CA January 30-31 2016
spark-timeseries
A library for time series analysis on Apache Spark
tuning_playbook
A playbook for systematically maximizing the performance of deep learning models.