Introduction to Deep Learning

From Logical Calculus to Artificial Intelligence

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Artificial Intelligence, General Computing
Cover of the book Introduction to Deep Learning by Sandro Skansi, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Sandro Skansi ISBN: 9783319730042
Publisher: Springer International Publishing Publication: February 4, 2018
Imprint: Springer Language: English
Author: Sandro Skansi
ISBN: 9783319730042
Publisher: Springer International Publishing
Publication: February 4, 2018
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer International Publishing

Cover of the book Mobile Web and Intelligent Information Systems by Sandro Skansi
Cover of the book Dynamics of Mathematical Models in Biology by Sandro Skansi
Cover of the book Designing Impedance Networks Converters by Sandro Skansi
Cover of the book Potable Water by Sandro Skansi
Cover of the book Advances in Human Factors in Sports, Injury Prevention and Outdoor Recreation by Sandro Skansi
Cover of the book Developmental Universities in Inclusive Innovation Systems by Sandro Skansi
Cover of the book Amplify-and-Forward Relaying in Wireless Communications by Sandro Skansi
Cover of the book Popular High Culture in Italian Media, 1950–1970 by Sandro Skansi
Cover of the book Blurring the Boundaries Through Digital Innovation by Sandro Skansi
Cover of the book Refinement Monoids, Equidecomposability Types, and Boolean Inverse Semigroups by Sandro Skansi
Cover of the book Physical Principles of Electron Microscopy by Sandro Skansi
Cover of the book The Vascular Flora of the National Park of Abruzzo, Lazio and Molise (Central Italy) by Sandro Skansi
Cover of the book Management and Leadership – A Guide for Clinical Professionals by Sandro Skansi
Cover of the book Atlas of Wide-Field Retinal Angiography and Imaging by Sandro Skansi
Cover of the book PET/CT in Gynecological Cancers by Sandro Skansi
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy