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Python Machine Learning

You're reading from   Python Machine Learning Learn how to build powerful Python machine learning algorithms to generate useful data insights with this data analysis tutorial

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Product type Paperback
Published in Sep 2015
Publisher Packt
ISBN-13 9781783555130
Length 454 pages
Edition 1st Edition
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Author (1):
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Sebastian Raschka Sebastian Raschka
Author Profile Icon Sebastian Raschka
Sebastian Raschka
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Toc

Table of Contents (15) Chapters Close

Preface 1. Giving Computers the Ability to Learn from Data 2. Training Machine Learning Algorithms for Classification FREE CHAPTER 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. Training Artificial Neural Networks for Image Recognition 13. Parallelizing Neural Network Training with Theano Index

Other neural network architectures

In this chapter, we discussed one of the most popular feedforward neural network representations, the multi-layer perceptron. Neural networks are currently one of the most active research topics in the machine learning field, and there are many other neural network architectures that are well beyond the scope of this book. If you are interested in learning more about neural networks and algorithms for deep learning, I recommend reading the introduction and overview; Y. Bengio. Learning Deep Architectures for AI. Foundations and Trends in Machine Learning, 2(1):1–127, 2009. Yoshua Bengio's book is currently freely available at http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf.

Although neural networks really are a topic for another book, let's take at least a brief look at two other popular architectures, convolutional neural networks and recurrent neural networks.

Convolutional Neural Networks

Convolutional Neural Networks (CNNs...

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