Neural Networks and Deep Learning (Springer)
Monday, 19 November 2018

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. Author Charu C. Aggarwal looks at why neural networks work, and when they work better than off-the-shelf machine-learning models. He also considers when depth is useful, why training neural networks is so hard, and what the pitfalls are. The book also looks at different applications to give a flavor of how neural architectures are designed for different types of problems such as recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics.

<ASIN:3319944622>

 

Author: Charu C. Aggarwal
Publisher: Springer
Date: August 2018
Pages: 497
ISBN: 978-3319944623
Print: 3319944622
Kindle:  B07FKF5HY7
Audience: graduate students, researchers, and neural network practitioners
Level: Advanced
Category:Artificial Intelligence 

 

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Print: 0133118975
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