Table of contents

  1. Front Matter
    Pages i-xiii
  2. Sandro Skansi
    Pages 1-16
  3. Sandro Skansi
    Pages 51-77
  4. Sandro Skansi
    Pages 79-105
  5. Sandro Skansi
    Pages 121-133
  6. Sandro Skansi
    Pages 135-152
  7. Sandro Skansi
    Pages 153-163
  8. Sandro Skansi
    Pages 165-173
  9. Sandro Skansi
    Pages 185-187
  10. Back Matter
    Pages 189-191

About this book


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.

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.


Deep learning Neural networks Pattern recognition Natural language processing Autoencoders

Authors and affiliations

  1. 1.University of ZagrebZagrebCroatia

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-73003-5
  • Online ISBN 978-3-319-73004-2
  • Series Print ISSN 1863-7310
  • Series Online ISSN 2197-1781
  • Buy this book on publisher's site