Description
A classic and academic reference, Neural Networks and Deep Learning: A Textbook provides a comprehensive and in-depth review of traditional and modern models in the field of deep learning. The book focuses on neural network theories and algorithms, an understanding of which is crucial for designing complex architectures in various applications. The author attempts to answer fundamental questions in the field; such as why neural networks work, when they are better than traditional machine learning models, why the process of training networks is difficult, and what challenges and pitfalls exist along the way.
In addition to theoretical topics, the book is very rich in terms of practical applications to give professionals a concrete perspective on system design. Applied topics such as recommender systems, machine translation, image captioning, image classification, reinforcement learning-based games, and text analysis are covered in this work. The structure of the book is divided into three main parts: the first part deals with basic concepts and the relationship between traditional machine learning models and neural networks; the second part reviews fundamental topics of training and regularization; and the third part is dedicated to advanced topics such as convolutional networks, recurrent networks, and adversarial networks (GANs).
Book Features
- Deep focus on theory, algorithms, and design principles of deep learning architectures
- Analysis of the relationship between neural networks and traditional machine learning models such as SVM and regression
- A detailed review of training methods, optimization, and network regularization techniques
- Coverage of the most advanced models including convolutional neural networks (CNN) and recurrent neural networks (RNN)
- Providing a practical approach with numerous exercises for students and researchers
Book specifications
- Publisher: Springer
- Lecturer/Author: Charu C. Aggarwal
- Number of pages: 856
- Number of chapters: 12
- Format: PDF
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