Publication:
Neural Networks and Deep Learning : A Textbook
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Abstract
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 applications. 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.
Description
Authors
Aggarwal, Charu C.
Alternate Authors
Advisor
Place of publication
Cham, Switzerland
Publisher
Springer
Date
2018
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Keywords
Artificial intelligence , Neural networks , Machine learning
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Please use the UNETI DRM to download/borrow digital documents
