**Neural Network Toolbox for MATLAB **

**Book Description**

Neural networks are composed of simple elements operating in parallel. The biological nervous system inspires these elements. As in nature, the network function is mainly determined by the connections between components.

Changing the values of the connections (weights) between elements can teach a neural network to do a specific task.

The Neural Network Toolbox has algorithms, functions, and apps that can be used to create, train, visualize, and simulate neural networks. You can make classification, regression, clustering, dimensionality reduction, time-series forecasting, and modeling and control of dynamic systems.

The toolbox includes convolutional neural networks and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up the training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using the Parallel Computing Toolbox.

The Neural Network Toolbox is written so that if you read Chapter 2, Chapter 3, and Chapter 4, you can proceed to a later chapter, read it, and use its functions without difficulty. To make this possible, Chapter 2 presents the fundamentals of the neuron model, the architecture of neural networks. It will also discuss the notation used in the architecture. All of this is basic material. It is to your advantage to understand this Chapter 2 material thoroughly. The neuron model and the architecture of a neural network describe how a network transforms its input into an output. This transformation can be viewed as a computation. The model and the architecture each place limitations on what a particular neural network can compute. Before training methods for a network can be explained, it’s important to understand how the network works.

**Neural Network Toolbox For Use with MATLAB PDF**

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