Book Image

Python Machine Learning By Example - Third Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example - Third Edition

By: Yuxi (Hayden) Liu

Overview of this book

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.
Table of Contents (17 chapters)
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Getting started with CNN building blocks

Although regular hidden layers (the fully connected layers we have seen so far) do a good job of extracting features from data at certain levels, these representations might be not useful in differentiating images of different classes. CNNs can be used to extract richer and more distinguishable representations that, for example, make a car a car, a plane a plane, or the handwritten letters "y" a "y", "z" a "z", and so on. CNNs are a type of neural network that is biologically inspired by the human visual cortex. To demystify CNNs, I will start by introducing the components of a typical CNN, including the convolutional layer, the nonlinear layer, and the pooling layer.

The convolutional layer

The convolutional layer is the first layer in a CNN, or the first few layers in a CNN if it has multiple convolutional layers. It takes in input images or matrices and simulates the way neuronal cells respond...