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Hands-On Automated Machine Learning

You're reading from   Hands-On Automated Machine Learning A beginner's guide to building automated machine learning systems using AutoML and Python

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788629898
Length 282 pages
Edition 1st Edition
Languages
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Authors (2):
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Umit Mert Cakmak Umit Mert Cakmak
Author Profile Icon Umit Mert Cakmak
Umit Mert Cakmak
Sibanjan Das Sibanjan Das
Author Profile Icon Sibanjan Das
Sibanjan Das
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to AutoML FREE CHAPTER 2. Introduction to Machine Learning Using Python 3. Data Preprocessing 4. Automated Algorithm Selection 5. Hyperparameter Optimization 6. Creating AutoML Pipelines 7. Dive into Deep Learning 8. Critical Aspects of ML and Data Science Projects 9. Other Books You May Enjoy

A feed-forward neural network using Keras

Keras is a DL library, originally built on Python, that runs over TensorFlow or Theano. It was developed to make DL implementations faster:

  1. We call install keras using the following command in your operation system's Command Prompt:
pip install keras
  1. We start by importing the numpy and pandas library for data manipulation. Also, we set a seed that allows us to reproduce the script's results:
import numpy as np
import pandas as pd
numpy.random.seed(8)
  1. Next, the sequential model and dense layers are imported from keras.models and keras.layers respectively. Keras models are defined as a sequence of layers. The sequential construct allows the user to configure and add layers. The dense layer allows a user to build a fully connected network:
from keras.models import Sequential
from keras.layers import Dense
  1. The HR attrition dataset...
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