In keeping with the word “practical” in the book’s title, we’ve focused heavily on the real-world aspects of deep learning. The goal of this appendix is meant to serve as reference material, rather than a full-fledged exploration into the theoretical aspects of deep learning. To develop a deeper understanding of some of these topics, we recommend perusing the “Further Exploration” for references to other source material.
Machine learning helps learn patterns from data to make predictions on unseen data.
There are three kinds of machine learning: supervised learning (learning from labeled data), unsupervised learning (learning from unlabeled data), and reinforcement learning (learning by action and feedback from an environment).
Supervised learning tasks include classification (output is one of many categories/classes) and regression (output is a numeric value).
There are various supervised machine learning techniques including naive Bayes, SVM, decision trees, k-nearest neighbors, neural networks, and others.
A perceptron, as shown in Figure A-1, is the simplest form of a neural network, a single-layered neural network with one neuron.
A perceptron calculates a weighted sum of its inputs; that is, it accepts input values, multiplies each with a corresponding weight, adds a bias term, and generates a numeric output.
Because a perceptron ...