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A **neural network** is an approach to **machine learning** which is built fundamentally on the principle of how signals are transmitted among the neurons in the **human brain**. Also known as an **artificial neural network**, it is a system that learns adaptively by using interconnected neurons or nodes assembled in a layered structured resembling the human brain. The neural network has the ability to learn from data which makes it capable of classifying data, recognizing patterns, and forecasting future events. It breaks down the data input into several layers of abstraction and can be trained using many different examples so that it can accurately recognize the patterns in images, speeches, etc., just like the human brain.

The behavior of a neural network is defined by the way in which the individual elements comprising the network are connected and by the weights or strengths of those connections. The weights are adjusted automatically during the course of training on the basis of specified rules of learning until the neural network starts performing the assigned task as required. Neural networks are among the most suitable and advanced approaches to modeling of non-linear relationships and performing tasks such as recognizing patterns and classifying signals or objects in vision, speech, and control systems.

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Neural networks, especially **deep neural networks** are being increasingly recognized for their usability and efficiency at tasks that require complex identification such as facial recognition, voice recognition, and text translations. They provide a key approach to technology which drives innovation in various fields of application, from advanced medical research to mobile communication and driver assistance technologies.

**Following are the examples where neural networks are used as a primary method of machine learning, demonstrating how important they are for advanced research and technology**:

- ➤ Semantic segmentation of images and videos.
- ➤ Detection of specific objects such as vehicles, cyclists and pedestrians in images and videos.
- ➤ Application of reinforcement learning to train biped robots to walk steadily.
- ➤ Detection of cancer by helping pathologists to classify tumors as malignant or benign on the basis of cell size uniformity, mitosis, clump thickness, and other critical factors.
- ➤ Development of advanced driver assistance systems and performance of tasks such as recognizing traffic signs and identifying lanes and obstacles.
- ➤ Development and deployment of facial recognition systems for surveillance and access authentication.
- ➤ Accurate predictions about the stock market in real time by employing a type of feed-forward artificial intelligence (AI) algorithm.
- ➤ Providing AI-based support for fault diagnosis, securing the control systems of aircrafts, high-performance auto piloting, and modeling simulations of key dynamics in aerospace engineering.
- ➤ Logistics, object location, armed attack analysis, air and maritime patrol, and automated drone control using AI technologies in defense systems.

Development of the first neural network dates back to 1958 when **Frank Rosenblatt** introduced the simple ‘**perceptron**’ which consisted of a linear regression model and a single neuron having a sigmoid activation function. More complex neural networks have been explored ever since which have led to the development of deep neural networks that contain practically hundreds of layers of neurons that can be seen today.

**Note that neural networks containing multiple layers of connected neurons are called deep neural networks whereas those containing just two to three layers are called shallow neural networks**.

**Some of the commonly used neural networks that can be developed using Matlab for various engineering applications include:**

- ➤
**Feed-forward Neural Network:**This type of neural network which usually a shallow neural network, consists of an input layer, one or more hidden layers, and an output layer. - ➤
**Convolutional Neural Network (CNN):**This is a deep neural network architecture which is commonly used for image processing. The CNN is characterized by convolutional layers which can shift windows encompassing the input with weight-sharing nodes. This allows the input (usually an image) to be abstracted to feature maps. - ➤
**Recurrent Neural Network (RNN):**This is again a type of deep neural network having feedback loops. The RNN is used for modeling sequential dependencies such as text data, time series, and sensor in the input. The long short-term memory network is the most widely used type of RNN.

Matlab provides several specialized toolboxes for neural networks, machine learning, deep learning, automated driving assistance, and computer vision. Matlab can be used even by non-experts to develop neural networks simply by writing a few lines of code. It allows users to start on a project quickly, create and visualize models, integrate the models into existing applications, and deploy them to enterprise systems, servers, embedded devices, clouds, and clusters. Developing a neural network in Matlab typically involves the following steps:

- ➤
**DATA PREPARATION HOMEWORK HELP:**The first step requires acquiring sufficient and labeled data for**training deep neural networks**. This process can be expedited using labeler apps including the Signal labeled, Video and Image. When acquiring it from real systems becomes impractical (such as due to failure conditions), Matlab allows users to generate the training data using simulations. They can also augment their data in order to include more variation in the training data. - ➤
**AI MODELLING OF NEURAL NETWORKS HOMEWORK HELP:**Users can apply the classification and regression learner function provided in the**Statistics and Machine Learning Toolbox**™ to interactively train shallow neural networks. Alternatively, they can use command-line functions which are recommended when they need to compare the performance of shallow networks with that of traditional machine learning algorithms, or when there is limited labeled training data available for use. Both shallow and deep neural networks can be specified and trained interactively using the command-line functions available in the**Deep Learning Toolbox**™ or the Deep Network Designer. Command-line functions are especially suited for developing deep neural network models, or when the user needs more flexibility to customize network solvers and architecture. - ➤
**SIMULATING AND TESTING NEURAL NETWORKS HOMEWORK HELP:**Matlab allows neural networks to be integrated into Simulink models as block objects. This integration then facilitates testing, deployment to several types of hardware, and integration of the network with larger systems. - ➤
**NEURAL NETWORKS DEPLOYMENT HOMEWORK HELP:**In the final step, shallow neural networks which are trained using the Statistics and Machine Learning Toolbox TM are used to write plain and simple C/C++ code for deploying the neural network to high-performance computer systems and embedded hardware. Plain C/C++ and optimized CUDA code can also be generated using neural networks trained with the Deep Learning Toolbox™ to achieve faster inferences on GPUs and industrial hardware such as FPGA and ARM.

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