szilard / datascience-latencyLinks
Latency numbers every data scientist should know (aka the pyramid of analytical tasks) - the order of magnitude of computational time for the most common analytical tasks (SQL-like data munging, linear and non-linear supervised learning etc.) with the typically available tools on commodity hardware.
☆20Updated 8 years ago
Alternatives and similar repositories for datascience-latency
Users that are interested in datascience-latency are comparing it to the libraries listed below
Sorting:
- Algorithm's team Jupyter Notebooks☆113Updated 2 months ago
- Code for Learning with Data Blog☆64Updated 8 years ago
- Example scripts for various deep learning APIs.☆28Updated 10 years ago
- Modeling Social Data, Applied Mathematics, Columbia University (Spring 2015)☆33Updated 6 years ago
- Latent dirichlet allocation (LDA) for datamicroscopes☆41Updated 9 years ago
- Talk on "Tree models with Scikit-Learn: Great learners with little assumptions" presented at PyPata Paris 2015☆50Updated 10 years ago
- Materials for my PyData Seattle talk☆21Updated 10 years ago
- Presentation at Perth Data Science Meetup, February 2015☆72Updated 10 years ago
- Quick informal survey at the Los Angeles Machine learning meetup about tools used for machine learning.☆51Updated 10 years ago
- Repo for experiments on pyspark and sklearn☆79Updated 11 years ago
- Docker images for data science from Wise.io☆50Updated 9 years ago
- Advanced workshop on XGBoost with Tianqi Chen in Santa Monica, June 2, 2016☆26Updated 8 years ago
- Machine Learning with Scikit-Learn (material for pydata Amsterdam 2016)☆30Updated 9 years ago
- PDF and python files for creating time maps and downloading tweets☆59Updated 5 years ago
- Source code for the tutorial series at http://www.thoughtly.co/blog/prototype☆32Updated 10 years ago
- Spark library for doing exploratory data analysis in a scalable way☆43Updated 9 years ago
- Distributed Matrix Library☆72Updated 8 years ago
- Using data to dig into the 2015 NL Cy Young race☆10Updated 9 years ago
- ☆28Updated 9 years ago
- ☆34Updated 9 years ago
- Notebooks (and slides) for my PyData NYC 2014 tutorial on the more advanced features of scikit-learn.☆69Updated 10 years ago
- Creates models to classify documents into categories☆66Updated 7 years ago
- training material☆47Updated 10 months ago
- Material for some talks I have given☆62Updated 11 months ago
- Install directions and example notebooks for Udacity's Deep Learning classes☆28Updated 9 years ago
- Code for PyData Talk on "Classifying Products Based on Images and Text using Keras"☆30Updated 8 years ago
- Python forecasting and smoothing library☆67Updated 6 years ago
- Fast, accurate, lightweight, multi-core ML in Python, leveraging Vowpal Wabbit☆21Updated 7 years ago
- Fast, easy and intuitive machine learning prototyping.☆124Updated 11 years ago
- ☆24Updated 9 years ago