Book Image

Python Machine Learning - Third Edition

By : Sebastian Raschka, Vahid Mirjalili
5 (1)
Book Image

Python Machine Learning - Third Edition

5 (1)
By: Sebastian Raschka, Vahid Mirjalili

Overview of this book

Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.
Table of Contents (21 chapters)
20
Index

Modeling Sequential Data Using Recurrent Neural Networks

In the previous chapter, we focused on convolutional neural networks (CNNs). We covered the building blocks of CNN architectures and how to implement deep CNNs in TensorFlow. Finally, you learned how to use CNNs for image classification. In this chapter, we will explore recurrent neural networks (RNNs) and see their application in modeling sequential data.

We will cover the following topics:

  • Introducing sequential data
  • RNNs for modeling sequences
  • Long short-term memory (LSTM)
  • Truncated backpropagation through time (TBPTT)
  • Implementing a multilayer RNN for sequence modeling in TensorFlow
  • Project one: RNN sentiment analysis of the IMDb movie review dataset
  • Project two: RNN character-level language modeling with LSTM cells, using text data from Jules Verne's The Mysterious Island
  • Using gradient clipping to avoid exploding gradients
  • Introducing the Transformer model and understanding...