Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence

✍ Scribed by Sandro Skansi (auth.)

• Publisher: Springer International Publishing
• Year: 2018
• Tongue: English
• Leaves: 196
• Series: Undergraduate Topics in Computer Science
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xiii
From Logic to Cognitive Science (Sandro Skansi)....Pages 1-16
Mathematical and Computational Prerequisites (Sandro Skansi)....Pages 17-49
Machine Learning Basics (Sandro Skansi)....Pages 51-77
Feedforward Neural Networks (Sandro Skansi)....Pages 79-105
Modifications and Extensions to a Feed-Forward Neural Network (Sandro Skansi)....Pages 107-120
Convolutional Neural Networks (Sandro Skansi)....Pages 121-133
Recurrent Neural Networks (Sandro Skansi)....Pages 135-152
Autoencoders (Sandro Skansi)....Pages 153-163
Neural Language Models (Sandro Skansi)....Pages 165-173
An Overview of Different Neural Network Architectures (Sandro Skansi)....Pages 175-183
Conclusion (Sandro Skansi)....Pages 185-187
Back Matter ....Pages 189-191

✦ Subjects

Data Mining and Knowledge Discovery