Book Image

Python Machine Learning - Third Edition

By : Sebastian Raschka, Vahid Mirjalili
5 (2)
Book Image

Python Machine Learning - Third Edition

5 (2)
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

Building an NN model in TensorFlow

So far in this chapter, you have learned about the basic utility components of TensorFlow for manipulating tensors and organizing data into formats that we can iterate over during training. In this section, we will finally implement our first predictive model in TensorFlow. As TensorFlow is a bit more flexible but also more complex than machine learning libraries such as scikit-learn, we will start with a simple linear regression model.

The TensorFlow Keras API (tf.keras)

Keras is a high-level NN API and was originally developed to run on top of other libraries such as TensorFlow and Theano. Keras provides a user-friendly and modular programming interface that allows easy prototyping and the building of complex models in just a few lines of code. Keras can be installed independently from PyPI and then configured to use TensorFlow as its backend engine. Keras is tightly integrated into TensorFlow and its modules are accessible through tf.keras...