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Python Machine Learning by Example

You're reading from   Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

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Product type Paperback
Published in Oct 2020
Publisher Packt
ISBN-13 9781800209718
Length 526 pages
Edition 3rd Edition
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Author (1):
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Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
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Table of Contents (17) Chapters Close

Preface 1. Getting Started with Machine Learning and Python 2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER 3. Recognizing Faces with Support Vector Machine 4. Predicting Online Ad Click-Through with Tree-Based Algorithms 5. Predicting Online Ad Click-Through with Logistic Regression 6. Scaling Up Prediction to Terabyte Click Logs 7. Predicting Stock Prices with Regression Algorithms 8. Predicting Stock Prices with Artificial Neural Networks 9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques 10. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling 11. Machine Learning Best Practices 12. Categorizing Images of Clothing with Convolutional Neural Networks 13. Making Predictions with Sequences Using Recurrent Neural Networks 14. Making Decisions in Complex Environments with Reinforcement Learning 15. Other Books You May Enjoy
16. Index

Demystifying neural networks

Here comes probably the most frequently mentioned model in the media, artificial neural networks (ANNs); more often we just call them neural networks. Interestingly, the neural network has been (falsely) considered equivalent to machine learning or artificial intelligence by the general public.

The ANN is just one type of algorithm among many in machine learning. And machine learning is a branch of artificial intelligence. It is one of the ways we achieve general artificial intelligence.

Regardless, it is one of the most important machine learning models and has been rapidly evolving along with the revolution of deep learning (DL). Let's first understand how neural networks work.

Starting with a single-layer neural network

I will first talk about different layers in a network, then the activation function, and finally training a network with backpropagation.

Layers in neural networks

A simple...

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