Book Image

Python Machine Learning, Second Edition - Second Edition

By : Sebastian Raschka, Vahid Mirjalili
Book Image

Python Machine Learning, Second Edition - Second Edition

By: Sebastian Raschka, Vahid Mirjalili

Overview of this book

Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new third edition, updated for 2020 and featuring TensorFlow 2 and the latest in scikit-learn, reinforcement learning, and GANs, has now been published. Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Using Python's open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities. If you’ve read the first edition of this book, you’ll be delighted to find a balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You’ll be able to learn and work with TensorFlow 1.x more deeply than ever before, and get essential coverage of the Keras neural network library, along with updates to scikit-learn 0.18.1.
Table of Contents (18 chapters)
17
Index

Summary

In this chapter, you learned how to use TensorFlow, an open source library for numerical computations with a special focus on deep learning. While TensorFlow is more inconvenient to use compared to NumPy, due to its additional complexity to support GPUs, it allows us to define and train large, multilayer neural networks very efficiently.

Also, you learned about the TensorFlow API to build complex machine learning and neural network models and run them efficiently. First, we explored programming in the low-level TensorFlow API. Implementing models at this level may be tedious when we have to program at the level of matrix-vector multiplications and define every detail of each operation. However, the advantage is that this allows us as developers to combine such basic operations and build more complex models. Furthermore, we discussed how TensorFlow allows us to utilize the GPUs for training and testing big neural networks to speed up the computations. Without the use of GPUs, training...