https://github.com/glemaitre/dssp_10_2019

https://github.com/glemaitre/dssp_10_2019

Last synced: 8 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/glemaitre/dssp_10_2019
• Owner: glemaitre
• License: cc0-1.0
• Created: 2019-10-14T14:52:24.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2019-10-18T12:41:15.000Z (about 5 years ago)
• Last Synced: 2024-10-28T02:05:11.240Z (about 2 months ago)
• Language: Jupyter Notebook
• Size: 2.77 MB
• Stars: 1
• Watchers: 4
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# DSSP 13

## Curriculum

This lecture is focused on the following concepts:

* understand tree-based algorithms;

* manage mixed data types in machine-learning pipeline;

* fine tuning model by hyper-parameters search.

## Getting started

In case that you have any issues, you click on the binder link below

which will setup an online machine for you:

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/glemaitre/dssp_10_2019/master)

Alternatively you can create a new conda environment which will be called

`dssp` by default and whill contain all the packages required to run the

notebooks:

``` bash

conda env create -f environment.yml

conda activate dssp

```

```bash

cd path/to/dssp_10_2019

jupyter notebook

```

You can also update an existing `conda` environment:

``` bash

conda env update -f environment.yml

```

## References

This material is inspired and reused part of the following materials:

* https://github.com/amueller/scipy-2018-sklearn

* https://github.com/lesteve/euroscipy-2019-scikit-learn-tutorial