CONVOLUTIONAL NEURAL NETWORKS Examples with MATLAB

CONVOLUTIONAL NEURAL NETWORKS Examples with MATLABConvolution neural networks (CNNs or ConvNets) are essential tools for deep learning, and are especially suited for image recognition. You can construct a CNN architecture, train a network, and use the trained network to predict class labels. You can also extract features from a pre trained network, and use these features to train a linear classifier. Neural Network Toolbox also enables you to perform transfer learning; that is, retrain the last fully

Convolution neural networks (CNNs or ConvNets) are essential tools for deep learning, and are especially suited for image recognition. You can construct a CNN architecture, train a network, and use the trained network to predict class labels. You can also extract features from a pre-trained network, and use these features to train a linear classifier. Neural Network Toolbox also enables you to perform transfer learning; that is, retrain the last fully connected layer of an existing CNN on new data. MATLAB has the tool Neural Network Toolbox (Deep Leraning toolbox fron release 18) that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. This book develops the treatment and applications of convolutional neural networks with MATLAB through functions, classes, image classification, feature learning, pattern recognition, clustering, autoencoders, transfer learning and deep learning.