Introduction to Deep Learning

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 learningDiscusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural networkExamines convolutional neural networks, and the recurrent connections to a feed-forward neural networkDescribes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learningPresents 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.


Dr. Sandro Skansi is an Assistant Professor of Logic at the University of Zagreb and Lecturer in Data Science at University College Algebra, Zagreb, Croatia.