GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → amueller → Pydata Amsterdam 2016

amueller / Pydata Amsterdam 2016

Licence: cc0-1.0
Machine Learning with Scikit-Learn (material for pydata Amsterdam 2016)

Labels

jupyter-notebook

Projects that are alternatives of or similar to Pydata Amsterdam 2016

Medium blog
Stars: ✭ 30 (-6.25%)
Mutual labels:  jupyter-notebook
Ndap Fa2018
neuro data analysis in python
Stars: ✭ 32 (+0%)
Mutual labels:  jupyter-notebook
Mooc Neurons And Synapses 2017
Reference data for the "Simulation Neuroscience:Neurons and Synapses" Massive Online Open Course
Stars: ✭ 32 (+0%)
Mutual labels:  jupyter-notebook
Jax Fenics Adjoint
Differentiable interface to FEniCS for JAX using dolfin-adjoint/pyadjoint
Stars: ✭ 32 (+0%)
Mutual labels:  jupyter-notebook
Pytorch Mnist Vae
Stars: ✭ 32 (+0%)
Mutual labels:  jupyter-notebook
Tf eager
Exercises for tf (eager) learning
Stars: ✭ 32 (+0%)
Mutual labels:  jupyter-notebook
Discogan Pytorch
PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"
Stars: ✭ 961 (+2903.13%)
Mutual labels:  jupyter-notebook
2016learnpython
Python Teaching, Seminars for 2nd year students of School of Linguistics NRU HSE
Stars: ✭ 32 (+0%)
Mutual labels:  jupyter-notebook
Seismic Transfer Learning
Deep-learning seismic facies on state-of-the-art CNN architectures
Stars: ✭ 32 (+0%)
Mutual labels:  jupyter-notebook
Densedepth
High Quality Monocular Depth Estimation via Transfer Learning
Stars: ✭ 963 (+2909.38%)
Mutual labels:  jupyter-notebook
A Music Taste Analysis Using Spotify Api And Python.
Exploring Audio Features and building a Machine Learning Approach
Stars: ✭ 32 (+0%)
Mutual labels:  jupyter-notebook
Pn2v
This is our implementation of Probabilistic Noise2Void
Stars: ✭ 32 (+0%)
Mutual labels:  jupyter-notebook
Mckinsey Analytics Online Hackathon
Source Code for McKinsey Analytics Online Hackathon
Stars: ✭ 32 (+0%)
Mutual labels:  jupyter-notebook
Deeplabv3 Plus
Tensorflow 2.3.0 implementation of DeepLabV3-Plus
Stars: ✭ 32 (+0%)
Mutual labels:  jupyter-notebook
Ml Weekly
My weekly of machine learning. Collection of implemented algorithms.
Stars: ✭ 32 (+0%)
Mutual labels:  jupyter-notebook
2015 Python
(Winter 2015) Python for Data Mining mini-course.
Stars: ✭ 31 (-3.12%)
Mutual labels:  jupyter-notebook
Itri Speech Recognition Dataset Generation
Automatic Speech Recognition Dataset Generation
Stars: ✭ 32 (+0%)
Mutual labels:  jupyter-notebook
Pyhat
Python Hyperspectral Analysis Tools
Stars: ✭ 32 (+0%)
Mutual labels:  jupyter-notebook
Musings
Repo of some side-issues/Sub-problems and their solutions I have encountered in my ML work in past few years.
Stars: ✭ 32 (+0%)
Mutual labels:  jupyter-notebook
Nbmultitask
multithreading/multiprocessing with ipywidgets and jupyter notebooks
Stars: ✭ 32 (+0%)
Mutual labels:  jupyter-notebook
View All Similar Projects ➔

Machine Learning in Python with scikit-learn

This repository contains material for the PyData Amsterdam 2016 tutorial

Machine learning in Python with scikit-learn

by Andreas Mueller.

13:30-14:15 Saturday, 12/03/2016

Location: Room2

It is recommended that you update the materials before the course, as they might change in the days leading up to the conference.

Please bring a laptop with a working installation of Python (2.7, 3.4 or 3.5). The following packages are required:

  • scikit-learn >= 0.16
  • matplotlib >= 1.3
  • numpy >= 1.5
  • IPython >= 4.0
  • Jupyter Notebook >= 4.0

The easiest way to install all requirements is to install the free Anaconda Python distribution: https://www.continuum.io/downloads (OS X, Windows, Linux)

Please download the material prior to arriving to the tutorial, and make sure you can run the notebooks. To run a notebook, start Jupyter Notebook and browse to the folder to which you downloaded it.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].