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Artificial Neural Networks (ANN)

                 It is artificially created efficient computing systems designed to simulate the human brain. It includes machine
                 learning as part of Artificial Intelligence. An ANN in its training phase is capable of learning by recognising patterns
                 in data which is later used to generate the desired output.
                 ANN is made up of three basic layers – Input, Hidden and Output. The input layer accepts the inputs, the hidden
                 layer processes the inputs, and the output layer produces the result where each layer tries to learn from the
                 computed weights. It is the foundation of AI and is used to solve complex problems that are difficult for humans.
                 It consists of hardware or software that operates just like neurons of the human brain. Commercial application of
                 ANN is in solving complex signal processing, predictions or pattern recognition problems.

                 Convolutional Neural Networks (CNN)


                 The Convolutional Neural Network is part of the Neural Networks that is primarily used for image related tasks. It
                 extracts spatial features from data. It is used in Image classification (e.g., object detection), Medical imaging (e.g.,
                 tumor detection), Facial recognition, Autonomous vehicles, etc.

                         What is Neural Network?


                 Neural Networks are loosely modelled after how neurons in the human brain behave. The key advantage of Neural
                 Networks is that they are able to extract data features automatically without needing the input of the programmer.
                 A Neural Network is essentially a system of organising Machine Learning algorithms to perform certain tasks. It is
                 a fast and efficient way to solve problems for which the data set is very large, such as in images.
                 Neural Networks form a base of Deep Learning, a subfield of Machine Learning where algorithms are inspired by
                 the structure of the human brain. Neural networks take in data, train themselves to recognise the patterns in this
                 data and then predict the outputs for a new set of similar data. The most impressive aspect of Neural Networks is
                 that once trained, they learn on their own just like human brains.
                 Working of Neural Networks

                 Neural Networks are made up of layers of neurons, just like the human brain that consists of millions of neurons.
                 These neurons are the core processing units of the network. A Neural Network is divided into multiple layers.
                 Each layer in Neural Network is further divided into several blocks called nodes. Each node has its own task to
                 accomplish which is then passed to the next layer. These layers with their working are as follows:
                 1.   Input Layer: The input layer is the first layer of a Neural Network. Its job is to receive raw data from the outside
                    world and pass it into the network. No processing happens at this layer; it simply acts as a gateway for the data
                    to enter the system.
                 2.   Hidden Layers: The hidden layers come after the input layer where the actual processing happens. These
                    layers are called "hidden" because they are not directly visible to the user.
                       • Each node (or block) in a hidden layer performs a computation using weights and biases.
                       • The data received from the input layer is processed by applying Machine Learning algorithms at each node.

                       • After computation, the results are passed through an activation function, which determines whether the
                      information should be forwarded to the next layer.

                       • Over  multiple iterations,  the  network  adjusts the  weights  and  biases to  reduce  the  error  between  the
                      predicted output and the desired output. This trial-and-error process helps the network learn and improve
                      over time.




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