revan / RPi-distributed-MLLinks
Distributed Machine Learning Test Bench with Raspberry Pis
☆10Updated 9 years ago
Alternatives and similar repositories for RPi-distributed-ML
Users that are interested in RPi-distributed-ML are comparing it to the libraries listed below
Sorting:
- PyData London 2016 material☆37Updated 9 years ago
- Code for Pythonic visualization blog post☆40Updated 8 years ago
- How to Bootstrap Internal Applications With IPython Widgets (PyData 2015)☆19Updated 9 years ago
- Latent dirichlet allocation (LDA) for datamicroscopes☆41Updated 9 years ago
- Dask powered gridsearch and pipeline a la scikit-learn☆42Updated 9 years ago
- ☆67Updated 8 years ago
- Code and notes from using scikit-learn on the MNIST digits dataset. For more of a narrative on this project, see the article:☆29Updated 9 years ago
- Docker container with a PyData stack and JupyterHub server☆37Updated 9 years ago
- Common post-estimation tasks for scikit-learn☆17Updated 8 years ago
- Evaluate Predictive APIs☆21Updated 9 years ago
- Materials for Mike's PyCon Canada 2016 PySpark Tutorial☆12Updated 8 years ago
- Repo for experiments on pyspark and sklearn☆79Updated 11 years ago
- Beginner's Guide to Machine Learning Competitions, EuroPython 2015, Tutorial☆29Updated 8 years ago
- ☆28Updated 8 years ago
- Benchmarks of the H2O Ensemble R interface (H2O 2.0).☆14Updated 4 years ago
- Python implementation of Markov Networks for neural computing.☆38Updated 4 months ago
- Algorithm's team Jupyter Notebooks☆113Updated last month
- Google Container Engine, JupyterHub, and Jupyter for classroom scenarios☆59Updated 7 years ago
- Articles on Data Science, Jupyter, and Pandas☆18Updated 9 years ago
- Healthcare Twitter Analysis☆26Updated 9 years ago
- Latency numbers every data scientist should know (aka the pyramid of analytical tasks) - the order of magnitude of computational time for…☆20Updated 8 years ago
- Experimental docker-compose setup to bootstrap distributed on a docker-swarm cluster.☆91Updated 7 years ago
- Deep learning for hackers: a hands-on approach to machine learning and deep learning.