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

First steps with TensorFlow

In this section, we will take our first steps in using the low-level TensorFlow API. After installing TensorFlow, we will cover how to create tensors in TensorFlow and different ways of manipulating them, such as changing their shape, data type, and so on.

Installing TensorFlow

Depending on how your system is set up, you can typically just use Python's pip installer and install TensorFlow from PyPI by executing the following from your terminal:

pip install tensorflow

This will install the latest stable version, which is 2.0.0 at the time of writing. In order to ensure that the code presented in this chapter can be executed as expected, it is recommended that you use TensorFlow 2.0.0, which can be installed by specifying the version explicitly:

pip install tensorflow==[desired-version]

In case you want to use GPUs (recommended), you need a compatible NVIDIA graphics card, along with the CUDA Toolkit and the NVIDIA cuDNN library to...