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This book is mentioning mostly all about gradient-based machine learning, and not other mathematical methods, including perceptron which has no gradient.
Methods of ML
Gradient-based neural network (neuralnet for short) is talked about in this book. Other methods, especially maths-based are mentioned only, eg. Support Vector Machine (SVM), Decision Tree, etc.
Devices for ML
ML models can be trained on CPU, but always better with GPU as GPU has thousands of cores instead of a few cores as in CPU, that many of cores because GPU cores are much simpler just for some maths operators and matrix.
Tasks in ML
Feature Engineering
Prepare data, formatting, etc.
Regular Model Design
Do simple or mid of model complexity, works with big data.
Expert Model Design
Make models of high complexity, works with huge data.
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