This textbook presents a concise, accessible and engaging first
introduction to deep learning, offering a wide range of connectionist
models which represent the current state-of-the-art. The text explores
the most popular algorithms and architectures in a simple and intuitive
style, explaining the mathematical derivations in a step-by-step manner.
The content coverage includes convolutional networks, LSTMs, Word2vec,
RBMs, DBNs, neural Turing machines, memory networks and autoencoders.
Numerous examples in working Python code are provided throughout the
book, and the code is also supplied separately at an accompanying
website.
Topics and features: introduces the fundamentals of machine learning,
and the mathematical and computational prerequisites for deep learning;
discusses feed-forward neural networks, and explores the modifications
to these which can be applied to any neural network; examines
convolutional neural networks, and the recurrent connections to a
feed-forward neural network; describes the notion of distributed
representations, the concept of the autoencoder, and the ideas behind
language processing with deep learning; presents a brief history of
artificial intelligence and neural networks, and reviews interesting
open research problems in deep learning and connectionism.
This clearly written and lively primer on deep learning is essential
reading for graduate and advanced undergraduate students of computer
science, cognitive science and mathematics, as well as fields such as
linguistics, logic, philosophy, and psychology.
