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All Projects → xiaoganghan → Awesome Feature Engineering

xiaoganghan / Awesome Feature Engineering

Licence: mit
A curated list of feature engineering techniques for image and text machine learning

Labels

deep-learningmachine-learningfeature-engineering

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awesome-feature-engineering Awesome

A curated list of feature engineering techniques for image and text machine learning.

Table of Contents

Image

brightness, contrast, saturation

sample implementations

  1. mxnet

use cases

  1. Updated! My 99.40% solution to Udacity Nanodegree project P2 (Traffic Sign Classification)

sample

spatial transformer

sample implementations

  1. Traffic sign recognition with Torch

use cases

  1. The power of Spatial Transformer Networks

sample

histogram equalization

sample implementations

  1. Traffic signs classification with a convolutional network

use cases

  1. Traffic signs classification with a convolutional network

input

output

flipping

sample implementations

  1. Traffic signs classification with a convolutional network

use cases

  1. Traffic signs classification with a convolutional network

1

2

rotation and projection

sample implementations

  1. Traffic signs classification with a convolutional network

use cases

  1. Traffic signs classification with a convolutional network

input

output

others

zooming, cropping, panning, minor color changes

Libraries

output

Text

stemmer

sample implementations

  1. nltk

use cases

  1. Q&A With Job Salary Prediction First Prize Winner Vlad Mnih

tf-idf

sample implementations

  1. sklearn

use cases

  1. Is That a Duplicate Quora Question?

svd

sample implementations

  1. sklearn

use cases

  1. Is That a Duplicate Quora Question?

PCA

word2vec

sample implementations

  1. gensim

use cases

  1. Is That a Duplicate Quora Question?
  2. Do-it-yourself NLP for bot developers (text classification, entity recognition)

pipeline

## General

general feature engineering

input

feature engieering pipelines

data exploration with Pandas

feed data to model

  1. Is That a Duplicate Quora Question?
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