Book Image

Machine Learning with PyTorch and Scikit-Learn

By : Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili
5 (7)
Book Image

Machine Learning with PyTorch and Scikit-Learn

5 (7)
By: Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili

Overview of this book

Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. 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 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, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch 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 (22 chapters)
20
Other Books You May Enjoy
21
Index

Choosing activation functions for multilayer neural networks

For simplicity, we have only discussed the sigmoid activation function in the context of multilayer feedforward NNs so far; we have used it in the hidden layer as well as the output layer in the MLP implementation in Chapter 11.

Note that in this book, the sigmoidal logistic function, , is referred to as the sigmoid function for brevity, which is common in machine learning literature. In the following subsections, you will learn more about alternative nonlinear functions that are useful for implementing multilayer NNs.

Technically, we can use any function as an activation function in multilayer NNs as long as it is differentiable. We can even use linear activation functions, such as in Adaline (Chapter 2, Training Simple Machine Learning Algorithms for Classification). However, in practice, it would not be very useful to use linear activation functions for both hidden and output layers, since we want to introduce...