This must-read text reference introduces the fundamental concepts of convolutional neural
networks (ConvNets) offering practical guidance on using libraries to implement ConvNets in
applications of traffic sign detection and classification. The work presents techniques for
optimizing the computational efficiency of ConvNets as well as visualization techniques to
better understand the underlying processes. The proposed models are also thoroughly evaluated
from different perspectives using exploratory and quantitative analysis. Topics and features:
explains the fundamental concepts behind training linear classifiers and feature learning
discusses the wide range of loss functions for training binary and multi-class classifiers
illustrates how to derive ConvNets from fully connected neural networks and reviews different
techniques for evaluating neural networks presents a practical library for implementing
ConvNets explaining how to use a Python interface for the library to create and assess neural
networks describes two real-world examples of the detection and classification of traffic
signs using deep learning methods examines a range of varied techniques for visualizing neural
networks using a Python interface provides self-study exercises at the end of each chapter
in addition to a helpful glossary with relevant Python scripts supplied at an associated
website. This self-contained guide will benefit those who seek to both understand the theory
behind deep learning and to gain hands-on experience in implementing ConvNets in practice. As
no prior background knowledge in the field is required to follow the material the book is
ideal for all students of computer vision and machine learning and will also be of great
interest to practitioners working on autonomous cars and advanced driver assistance systems.
