Beale M.H., Hagan M.T., Demuth H.B. MatLAB Neural Network Toolbox: User's Guide. R2018a

The MathWorks, Inc., 2018. — 558 p.The book presents the theory of neural networks, discusses their design and application, and makes considerable use of the MatLAB environment and Neural Network Toolbox software. Example programs from the book are used in various sections of this documentation.
Deep learning is a branch of machine learning that teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. Deep learning is especially suited for image recognition, which is important for solving problems such as facial recognition, motion detection, and many advanced driver assistance technologies such as autonomous driving, lane detection, pedestrian detection, and autonomous parking.
Neural Network Toolbox provides simple MatLAB commands for creating and interconnecting the layers of a deep neural network. Examples and pretrained networks make it easy to use MatLAB for deep learning, even without knowledge of advanced computer vision algorithms or neural networks.Neural Network Objects, Data, and Training Styles
Deep Networks
Deep Learning in the Cloud
Multilayer Neural Networks and Backpropagation Training
Dynamic Neural Networks
Control Systems
Radial Basis Neural Networks
Self-Organizing and Learning Vector Quantization Networks
Adaptive Filters and Adaptive Training
Advanced Topics
Historical Neural Networks
Neural Network Object Reference