Neural networks have been a mainstay of artificial intelligence since its earliest days. Now, exciting new technologies such as deep learning and convolution are taking neural networks in bold new directions. In this book, we will demonstrate the neural networks in a variety of real-world tasks such as image recognition and data science. We examine current neural network technologies, including ReLU activation, stochastic gradient descent, cross-entropy, regularization, dropout, and visualization.
Learning to Program with MATLAB: Building GUI Tools
To learn how to program a computer in a modern language with serious graphical capabilities,
is to take hold of a tool of remarkable flexibility that has the power to provide
profound insight. This text is primarily aimed at being a first course in programming, and
is oriented toward integration with science, mathematics, and...
How to Make a Robot
Learn the basics of modern robotics while building your own intelligent robot from scratch! You'll use inexpensive household materials to make the base for your robot, then add motors, power, wheels, and electronics.
But wait, it gets better: your creation is actually five robots in one! -- build...
Internet of Things and Data Analytics Handbook
This book examines the Internet of Things (IoT) and Data Analytics from a technical, application, and business point of view.
Internet of Things and Data Analytics Handbook describes essential technical knowledge, building blocks, processes, design principles, implementation, and marketing...
Image Processing Using Pulse-Coupled Neural Networks Humans have an outstanding ability to recognise, classify and discriminate
objects with extreme ease. For example, if a person was in a large classroom
and was asked to find the light switch it would not take more than a second or
two. Even if the light switch was located in a different place than the human
expected or it was...