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@amueller
amueller / knn_imputation_speed.ipynb
Created Aug 25, 2015
np.multiply test for knn imputation
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@amueller
amueller / magic_constructor_estimator.ipynb
Created Apr 14, 2015
No more double underscores in sklearn.
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@amueller
amueller / elkan_bench.py
Last active Aug 29, 2015
benching elkan k-means implementation
View elkan_bench.py
from sklearn.cluster import KMeans
from time import time
from sklearn.datasets import load_digits, fetch_mldata, load_iris, fetch_20newsgroups_vectorized
def bench_kmeans(data, n_clusters=5, init='random', n_init=1):
start = time()
km1 = KMeans(algorithm='lloyd', n_clusters=n_clusters, random_state=0, init=init, n_init=n_init).fit(X)
print("lloyd time: %f inertia: %f" % (time() - start, km1.inertia_))
start = time()
km2 = KMeans(algorithm='elkan', n_clusters=n_clusters, random_state=0, init=init, n_init=n_init).fit(X)
@amueller
amueller / sklearn_tutorial_draft.rst
Last active Aug 29, 2015
scipy scikit-learn tutorial draft
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Tutorial Topic

This tutorial aims to provide an introduction to machine learning and scikit-learn "from the ground up". We will start with basic concepts of machine learning and implementing these using scikit-learn. Going in detail through the characteristics of several methods, we will discuss how to pick an algorithm for your application, how to set its parameters, and how to evaluate performance.

Please provide a more detailed abstract of your tutorial (again, see last years tutorials).

Machine learning is the task of extracting knowledge from data, often with the goal to generalize to new, unseen data. Applications of machine learning now touch nearly every aspect of everyday life, from the face detection in our

View scipy_interpolation_weirdness.ipnb
{
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},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
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{
View scipy_interpolation_weirdness.ipnb
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
@amueller
amueller / shuffle_once.ipynb
Created Dec 11, 2014
Shuffle once benchmarks
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@amueller
amueller / reassignment_strageties.ipynb
Created Dec 24, 2013
Benchmarking differente minibatch reassignment stategies.
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@amueller
amueller / msrc_blog.ipynb
Last active Oct 25, 2016
ipython notebook for msrc segmentation tutorial
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