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)
Other Books You May Enjoy

Learning the RNN architecture by example

As you can imagine, RNNs stand out because of their recurrent mechanism. We will start with a detailed explanation of this in the next section. We will talk about different types of RNNs after that, along with some typical applications.

Recurrent mechanism

Recall that in feedforward networks (such as vanilla neural networks and CNNs), data moves one way, from the input layer to the output layer. In RNNs, the recurrent architecture allows data to circle back to the input layer. This means that data is not limited to a feedforward direction. Specifically, in a hidden layer of an RNN, the output from the previous time point will become part of the input for the current time point. The following diagram illustrates how data flows in an RNN in general:

Figure 13.1: The general form of an RNN

Such a recurrent architecture makes RNNs work well with sequential data, including time series (such as daily temperatures, daily product...