Introduction to Deep Learning From Logical Calculus to Artificial Intelligence
by
Skansi, Sandro. author. (orcid)0000-0002-3851-1186
Title
:
Introduction to Deep Learning From Logical Calculus to Artificial Intelligence
Author
:
Skansi, Sandro. author. (orcid)0000-0002-3851-1186
ISBN
:
9783319730042
Personal Author
:
Skansi, Sandro. author.
Physical Description
:
XIII, 191 p. 38 illus. online resource.
Series
:
Undergraduate Topics in Computer Science,
Contents
:
From Logic to Cognitive Science -- Mathematical and Computational Prerequisites -- Machine Learning Basics -- Feed-forward Neural Networks -- Modifications and Extensions to a Feed-forward Neural Network -- Convolutional Neural Networks -- Recurrent Neural Networks -- Autoencoders -- Neural Language Models -- An Overview of Different Neural Network Architectures -- Conclusion.
Abstract
:
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.
Subject Term
:
Data mining.
Optical pattern recognition.
Coding theory.
Computer vision.
Data Mining and Knowledge Discovery. http://scigraph.springernature.com/things/product-market-codes/I18030
Pattern Recognition. http://scigraph.springernature.com/things/product-market-codes/I2203X
Mathematical Models of Cognitive Processes and Neural Networks. http://scigraph.springernature.com/things/product-market-codes/M13100
Coding and Information Theory. http://scigraph.springernature.com/things/product-market-codes/I15041
Image Processing and Computer Vision. http://scigraph.springernature.com/things/product-market-codes/I22021
Added Corporate Author
:
SpringerLink (Online service)
Electronic Access
:
Library | Material Type | Item Barcode | Shelf Number | Status |
---|
IYTE Library | E-Book | 2084893-1001 | QA76.9 .D343 | Online Springer |