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Preface
Sound analysis comprises the recognition of discrete speech from multiple speakers.
Generation of images using Autoencoders and GANs is also covered. Reinforcement
learning is used to build a deep Q-learning network capable of learning
autonomously. Experiments are the essence of the book. Each net is augmented by
multiple variants that progressively improve the learning performance by changing
the input parameters, the shape of the network, loss functions, and algorithms used
for optimizations. Several comparisons between training on CPUs, GPUs and TPUs
are also provided. The book introduces you to the new field of AutoML where deep
learning models are used to learn how to efficiently and automatically learn how to
build deep learning models. One advanced chapter is devoted to the mathematical
foundation behind machine learning.
Machine learning, artificial intelligence,
and the deep learning Cambrian
explosion
Artificial intelligence (AI) lays the ground for everything this book discusses.
Machine learning (ML) is a branch of AI, and Deep learning (DL) is in turn
a subset within ML. This section will briefly discuss these three concepts, which
you will regularly encounter throughout the rest of this book.
AI denotes any activity where machines mimic intelligent behaviors typically
shown by humans. More formally, it is a research field in which machines aim
to replicate cognitive capabilities such as learning behaviors, proactive interaction
with the environment, inference and deduction, computer vision, speech recognition,
problem solving, knowledge representation, and perception. AI builds on elements
of computer science, mathematics, and statistics, as well as psychology and other
sciences studying human behaviors. There are multiple strategies for building AI.
During the 1970s and 1980s, ‘expert’ systems became extremely popular. The goal
of these systems was to solve complex problems by representing the knowledge
with a large number of manually defined if–then rules. This approach worked for
small problems on very specific domains, but it was not able to scale up for larger
problems and multiple domains. Later, AI focused more and more on methods based
on statistical methods that are part of ML.
ML is a subdiscipline of AI that focuses on teaching computers how to learn without
the need to be programmed for specific tasks. The key idea behind ML is that it is
possible to create algorithms that learn from, and make predictions on, data. There
are three different broad categories of ML:
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