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Python Machine Learning

You're reading from  Python Machine Learning

Product type Book
Published in Sep 2015
Publisher Packt
ISBN-13 9781783555130
Pages 454 pages
Edition 1st Edition
Languages
Author (1):
Sebastian Raschka Sebastian Raschka
Profile icon Sebastian Raschka

Table of Contents (21) Chapters

Python Machine Learning
Credits
Foreword
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Giving Computers the Ability to Learn from Data 2. Training 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. Training Artificial Neural Networks for Image Recognition 13. Parallelizing Neural Network Training with Theano Index

Artificial neurons – a brief glimpse into the early history of machine learning


Before we discuss the perceptron and related algorithms in more detail, let us take a brief tour through the early beginnings of machine learning. Trying to understand how the biological brain works to design artificial intelligence, Warren McCullock and Walter Pitts published the first concept of a simplified brain cell, the so-called McCullock-Pitts (MCP) neuron, in 1943 (W. S. McCulloch and W. Pitts. A Logical Calculus of the Ideas Immanent in Nervous Activity. The bulletin of mathematical biophysics, 5(4):115–133, 1943). Neurons are interconnected nerve cells in the brain that are involved in the processing and transmitting of chemical and electrical signals, which is illustrated in the following figure:

McCullock and Pitts described such a nerve cell as a simple logic gate with binary outputs; multiple signals arrive at the dendrites, are then integrated into the cell body, and, if the accumulated signal...

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