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What is a Neural Network ?
Neural network also known as the Artificial neural network. Artificial neural networks (ANN’s) have a node layer that contains an input layer, one or more than hidden layers and output layer. Each node is connected to each other and has associated weight and threshold.
Training data is used by neural networks to learn and increase their accuracy over time.
Neural networks are sometimes in terms of their depth, based on how many layers they have between input and output layers. These middle layers are also called hidden layers. So the neural network is used almost synonymously with deep learning. It describes the number of hidden nodes, input and outputs.
Types of Neural Network
Feed forward neural network
Radial basis function neural network
Convolutional neural network
Deconvolutional neural network
Modular neural network
Recurrent neural network
Feed forward neural network : A feedforward neural network is an artificial neural network wherein connections between the nodes do not form a cycle. The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network.
Radial basis function neural network : In the field of mathematical modelling, a radial basis function network is an artificial neural network that uses radial basis function as activation function. The output of the network is a linear combination of radial basis functions of the inputs and neuron parameters. Radial basis function networks have many uses, including function approximation, time series prediction classification and system control.
Convolutional neural network : In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN). CNN is most commonly used for image processing. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivalent responses known as feature maps.
Modular neural network : A modular neural network is an artificial neural network characterized by a series of independent neural networks moderated by some intermediary. Each independent neural network serves as a module and operates on separate inputs to accomplish some subtask of the task the network hopes to perform
Recurrent neural network : A recurrent neural network (RNN) is a class of artificial neural network where connections between nodes form a directed or undirected graph along a temporal sequence. This allows it to exhibit temporal dynamic behaviour. Derived from feedforward neural network, RNNs can use their internal state (memory) to process variable length sequences of inputs
How Does a Neural Network Work?
Deep learning algorithms that use neural networks normally do not need to write a code with specific rules that define what to expect from the input. Instead, the neural net learning algorithm learns by analysing a large number of labelled instances (i.e. data with "answers") provided during training and using this answer key to determine which input features are required to construct the proper output. After a sufficient number of examples have been processed, the neural network can begin to handle new, unknown inputs and accurately deliver results. Because the computer learns from experience, the more examples and types of inputs it sees, the more accurate the outputs become.
Let's take an example, a simple problem of trying to determine whether or not an image contains a cat. While a human may easily figure this out, teaching a computer to recognise a cat in an image using traditional approaches is significantly more challenging. Given the many different ways a cat could appear in a photograph, designing code to account for every eventuality is nearly hard. However, the application can apply a generic approach to analysing the content in an image utilising machine learning, notably neural networks.
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