We see that javascript is disabled or not supported by your browser - javascript is needed for important actions on the site. Read more

What's New - Home - Login

Member $avings: $81,822,410.47 | Books Available: 704,994 | Members Online: 100

Swap Used Books - Buy New Books at Great Prices!

How To Swap Books Sign Up Search submenu

PBS Market (New Books)

Gift Buying Guide

Book Browser Advanced Search Books Posted Today Member Book Reviews Award Winning Books NYT Best Sellers Amazon Best Sellers Most Traveled Copies Club Wish List

Discussion Forums Book Lists

Club Lists My Book Lists My Watched Lists Create a List

Blog Donations

School Donation Program In Memory of... Military Donation Program Friends of PBS

Box-O-Books Maps The Eclectic Pen Fun Stuff

20 Questions Sudoku Bookmark Creator

Top 100

Requests Posts Swappers Referrers Reviewers

Pulse of PBS Spread The Word

Invite Friends Bookmarks Facebook Page Facebook App More Ways...

Photo Gallery Recipes Club Tag Cloud Member Testimonials

Help Center submenu

How To Swap Books Browse Help Docs Ask the Librarian PBS Member Icons Live Help

Kiosk

PBS Market (New Books) Go Shopping Buy Credits Buy PBS Money Upgrade Membership Gift Certificates Transfer Credits

Need Help?

Search - Neural Networks and Deep Learning: A Textbook

Neural Networks and Deep Learning A Textbook
Author: Charu C. Aggarwal

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applica... more »tions. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

ISBN-13: 9783319944623
ISBN-10: 3319944622
Publication Date: 8/26/2018
Pages: 497
Edition: 1st ed. 2018
Rating:

0 stars, based on 0 rating

Publisher: Springer
Book Type: Hardcover
Members Wishing: 2
Reviews: Amazon | Write a Review

Genres:

Back to the Top ↑

Search - Neural Networks and Deep Learning: A Textbook

Loading...