CNNs, or ConvNets, are quite similar to regular neural networks. They are still made up of neurons with weights that can be learned from data. Each neuron receives some inputs and performs a dot product. They still have a loss function on the last fully connected layer. They can still use a nonlinearity function. All of the tips and techniques that we learned from the last chapter are still valid for CNN. As we saw in the previous chapter, a regular neural network receives input data as a single vector and passes through a series of hidden layers. Every hidden layer consists of a set of neurons, wherein every neuron is fully connected to all the other neurons in the previous layer. Within a single layer, each neuron is completely independent and they do not share any connections. The last fully connected layer, also called the output layer...
Practical Convolutional Neural Networks
By :
Practical Convolutional Neural Networks
By:
Overview of this book
Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models.
This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available.
Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision.
By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets.
Related Content you might be interested in
Current Title:
Practical Convolutional Neural Networks
Table of Contents (11 chapters)
Preface
Free Chapter
Deep Neural Networks – Overview
Introduction to Convolutional Neural Networks
Build Your First CNN and Performance Optimization
Popular CNN Model Architectures
Transfer Learning
Autoencoders for CNN
Object Detection and Instance Segmentation with CNN
GAN: Generating New Images with CNN
Attention Mechanism for CNN and Visual Models
Other Books You May Enjoy
Customer Reviews