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About neural networks and their applications.

- 1. CONCEPT OF NEURAL NINJAS NEURAL NINJAS NEURAL NINJAS & ITS APPLICATIONS NEURAL NEURAL NEURAL NETWORK NETWORK NETWORK
- 2. Key Points covered in this presentation What is Neural Network ? Why we use ? Neural Network v/s Conventional Computers Applications Interlink between Artificial and Biological NN Types of Neural Network Architecture & Working of Neural Network Conclusion
- 3. Artificial Neural Network (ANN) is an information processing paradigm that is inspired by biological nervous systems. It is composed of a large number of highly interconnected processing elements called neurons. An ANN is configured for some specific applications, such as pattern recognition , data classification etc. Neural networks are ideally suited to help people in solving complex problems in real-life situations. We can learn and model the relationships between inputs and outputs that are nonlinear and complex , make generalizations and inferences , reveal hidden relationships , patterns and predictions ; and model highly volatile data (such as financial time series data) and variances needed to predict rare events (such as fraud detection). What is Neural Network ? Why we use ? Neural Network v/s Conventional Computers Conventional computers use an algorithmic approach, but neural networks works similar to human brain and learns by example.
- 4. All mammalian brains consist of interconnected neurons that transmit electrochemical signals. Neurons have several components: the body, which includes a nucleus and dendrites; axons, which connect to other cells; and axon terminals or synapses, which transmit information or stimuli from one neuron to another. Combined, this unit carries out communication and integration functions in the nervous system. The human brain has a massive number of processing units (86 billion neurons) that enable the performance of highly complex functions. How the Biological Model of Neural Network Functions ?
- 5. ANNs are statistical models designed to adapt and self- program by using learning algorithms in order to understand and sort out concepts, images, and photographs. For processors to do their work, developers arrange them in layers that operate in parallel. The input layer is analogous to the dendrites in the human brain’s neural network. The hidden layer is comparable to the cell body and sits between the input layer and output layer (which is akin to the synaptic outputs in the brain). The hidden layer is where artificial neurons take in a set of inputs based on synaptic weight, which is the amplitude or strength of a connection between nodes. These weighted inputs generate an output through a transfer function to the output layer. How the Artificial Neural Network Functions ?
- 6. Types of Neural Networks : Feedforward Neural Network The feedforward neural network is one of the most basic artificial neural networks. In this ANN, the data or the input provided ravels in a single direction. It enters into the ANN through the input layer and exits through the output layer while hidden layers may or may not exist. So the feedforward neural network has a front propagated wave only and usually does not have backpropagation. Recurrent Neural Network The Recurrent Neural Network saves the output of a layer and feeds this output back to the input to better predict the outcome of the layer. The first layer in the RNN is quite similar to the feed-forward neural network and the recurrent neural network starts once the output of the first layer is computed. After this layer, each unit will remember some information from the previous step so that it can act as a memory cell in performing computations.
- 7. Convolutional Neural Network A Convolutional neural network has some similarities to the feed-forward neural network, where the connections between units have weights that determine the influence of one unit on another unit. But a CNN has one or more than one convolutional layers that use a convolution operation on the input and then pass the result obtained in the form of output to the next layer. CNN has applications in speech and image processing which is particularly useful in computer vision. Modular Neural Network A Modular Neural Network contains a collection of different neural networks that work independently towards obtaining the output with no interaction between them. Each of the different neural networks performs a different sub-task by obtaining unique inputs compared to other networks. The advantage of this modular neural network is that it breaks down a large and complex computational process into smaller components, thus decreasing its complexity while still obtaining the required output. Radial basis function Neural Network Radial basis functions are those functions that consider the distance of a point concerning the center. RBF functions have two layers. In the first layer, the input is mapped into all the Radial basis functions in the hidden layer and then the output layer computes the output in the next step. Radial basis function nets are normally used to model the data that represents any underlying trend or function.
- 8. To understand the concept of the architecture of an artificial neural network, we have to understand what a neural network consists of. In order to define a neural network that consists of a large number of artificial neurons, which are termed units arranged in a sequence of layers. Lets us look at various types of layers available in an artificial neural network. The architecture of an Artificial Neural Network Artificial Neural Network primarily consists of three layers:
- 9. Input Layer Hidden Layer: Output Layer: As the name suggests, it accepts inputs in several different formats provided by the programmer. The hidden layer presents in-between input and output layers. It performs all the calculations to find hidden features and patterns. The input goes through a series of transformations using the hidden layer, which finally results in output that is conveyed using this layer. The artificial neural network takes input and computes the weighted sum of the inputs and includes a bias. This computation is represented in the form of a transfer function. It determines weighted total is passed as an input to an activation function to produce the output. Activation functions choose whether a node should fire or not. Only those who are fired make it to the output layer. There are distinctive activation functions available that can be applied upon the sort of task we are performing.
- 10. Artificial Neural Network can be best represented as a weighted directed graph, where the artificial neurons form the nodes. The association between the neurons outputs and neuron inputs can be viewed as the directed edges with weights. The Artificial Neural Network receives the input signal from the external source in the form of a pattern and image in the form of a vector. These inputs are then mathematically assigned by the notations x(n) for every n number of inputs. Afterward, each of the input is multiplied by its corresponding weights ( these weights are the details utilized by the artificial neural networks to solve a specific problem ). In general terms, these weights normally represent the strength of the interconnection between neurons inside the artificial neural network. All the weighted inputs are summarized inside the computing unit. Working of Neural Network
- 11. If the weighted sum is equal to zero, then bias is added to make the output non-zero or something else to scale up to the system's response. Bias has the same input, and weight equals to 1. Here the total of weighted inputs can be in the range of 0 to positive infinity. Here, to keep the response in the limits of the desired value, a certain maximum value is benchmarked, and the total of weighted inputs is passed through the activation function. The activation function refers to the set of transfer functions used to achieve the desired output. There is a different kind of the activation function, but primarily either linear or non-linear sets of functions. Some of the commonly used sets of activation functions are the Binary, linear, and Tan hyperbolic sigmoidal activation functions.
- 12. General Model of Neural Network The following diagram represents the general model of ANN followed by its processing. For this general model of artificial neural network, the net input can be calculated as follows − The output can be calculated by applying the activation function over the net input. We will pass the net input calculated inside the function for the final output
- 13. APPLICATIONS Data Processing including filtering & blind signal seperation Game playing & decision making (chess , racing etc.) Pattern Recognition (Face & Object Identification ) Sequence Recognition (Speech & Hand written text Recognition) Data Validation & Risk Management
- 14. About: Finger prints are the unique pattern of ridges and valleys in every person’s fingers. Their patterns are permanent and unchangeable for whole life of a person. They are unique and the probability that two fingerprints are alike is only 1 in 1.9x10^15. Their uniqueness is used for identification of a person. Image acquisition: the acquired image is digitalized into 512x512image with each pixel assigned a particular gray scale value(raster image). One of the Application in detail : Finger Print Recognition Image Acquisition > Edge Detection > Ridge Extraction > Thining > Feature Extraction > Classification Edge detection and Thinning: These are preprocessing of the image , remove noise and enhance the image. Feature extraction: This is the step where we pointout the features such as ridge bifurcation and ridge endings of the finger print with the helpof neural network. Classification: here a classlabel is assigned to theimage depending on theextracted features.
- 15. Conclusion Store information on the entire network The ability to work with insufficient knowledge Good falt tolerance Distributed memory Gradual Corruption Ability to train machine The ability of parallel processing The Advantages of Neural Networks: Dependence Unexplained functioning of the network Assurance of proper network structure The difficulty of showing the problem to The duration of the network is unknown The Disadvantages of Neural Networks: the network Neural networks are suitable for predicting time series mainly because of learning only from examples, without any need to add additional information that can bring more confusion than prediction effect. Neural networks are able to generalize and are resistant to noise. On the other hand, it is generally not possible to determine exactly what a neural network learned and it is also hard to estimate possible prediction error.
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