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You're reading from Python Machine Learning, Second Edition - Second Edition

Product type Book

Published in Sep 2017

Publisher Packt

ISBN-13 9781787125933

Pages 622 pages

Edition 2nd Edition

Languages

Python

Concepts

Machine Learning

Authors (2):

Sebastian Raschka

Vahid Mirjalili

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Table of Contents (24) Chapters

Python Machine Learning Second Edition

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Preface

1. Giving Computers the Ability to Learn from Data

2. Training Simple Machine Learning Algorithms for Classification

3. A Tour of Machine Learning Classifiers Using scikit-learn

4. Building Good Training Sets – Data Preprocessing

5. Compressing Data via Dimensionality Reduction

6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning

7. Combining Different Models for Ensemble Learning

8. Applying Machine Learning to Sentiment Analysis

9. Embedding a Machine Learning Model into a Web Application

10. Predicting Continuous Target Variables with Regression Analysis

11. Working with Unlabeled Data – Clustering Analysis

12. Implementing a Multilayer Artificial Neural Network from Scratch

13. Parallelizing Neural Network Training with TensorFlow

14. Going Deeper – The Mechanics of TensorFlow

15. Classifying Images with Deep Convolutional Neural Networks

16. Modeling Sequential Data Using Recurrent Neural Networks

Index

Building a regression model

Since we've explored placeholders and variables, let's build an example model for regression analysis, similar to the one we created in Chapter 13, Parallelizing Neural Network Training with TensorFlow, where our goal is to implement a linear regression model: .

In this model, w and b are the two parameters of this simple regression model that need to be defined as variables. Note that x is the input to the model, which we can define as a placeholder. Furthermore, recall that for training this model, we need to formulate a cost function. Here, we use the Mean Squared Error (MSE) cost function that we defined in Chapter 10, Predicting Continuous Target Variables with Regression Analysis .

Here, y is the true value, which is given as the input to this model for training. Therefore, we need to define y as a placeholder as well. Finally, is the prediction output, which will be computed using TensorFlow operations—tf.matmul and tf.add. Recall that TensorFlow operations...