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

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 than NumPy, due to its additional complexity to support GPUs, it allows us to define and train large, multilayer NNs very efficiently.

Also, you learned about using the TensorFlow Keras API to build complex machine learning and NN models and run them efficiently. We explored model building in TensorFlow by defining a model from scratch via subclassing the tf.keras.Model class. Implementing models can 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. We then explored tf.keras.layers, which makes building NN models a lot easier than implementing them from scratch.

Finally, you learned about...