Introduction to Artificial Neural Networks.pptx

Introduction to Artificial Neural Networks.pptx

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  Introduction to Artificial NeuralNetworks
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  ● Neural networksare inspired by biological neurons in the human brain and are composed of layers of connected nodes called “neurons” that contain mathematical functions to process incoming data and predict an output value. Neural networks:
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  Introduction: The term "ArtificialNeural Network" is derived from Biological neural networks that develop the structure of a human brain. Similar to the human brain that has neurons interconnected to one another, artificial neural networks also have neurons that are interconnected to one another in various layers of the networks. These neurons are known as nodes. Biological Neural Network Artificial Neural Network Dendrites Inputs Cell nucleus Nodes Synapse Weights Axon Output
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  What is ArtificialNeural Network(ANN)? An Artificial Neural Network (ANN) is a computational model inspired by the human brain’s neural structure. It consists of interconnected nodes (neurons) organized into layers. Information flows through these nodes, and the network adjusts the connection strengths (weights) during training to learn from data, enabling it to recognize patterns, make predictions, and solve various tasks in machine learning and artificial intelligence.
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  Artificial Neural NetworksArchitecture: There are three layers in the network architecture: the input layer, the hidden layer (more than one), and the output layer. Because of the numerous layers are sometimes referred to as the MLP (Multi-Layer Perceptron).
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  Input Layer: As thename suggests, it accepts inputs in several different formats provided by the programmer. Hidden Layer: The hidden layer presents in-between input and output layers. It performs all the calculations to find hidden features and patterns. Output Layer: The input goes through a series of transformations using the hidden layer, which finally results in output that is conveyed using this layer.
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  3. The activationfunction is important for two reasons: first, it allows you to turn on your computer. •This model captures the presence of non-linear relationships between the inputs. •It contributes to the conversion of the input into a more usable output.
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  Benefits of ArtificialNeural Networks: ANNs offers many key benefits that make them particularly well-suited to specific issues and situations: • ANNs can learn and model non-linear and complicated interactions, which is critical since many of the relationships between inputs and outputs in real life are non-linear and complex. • ANNs can generalize – After learning from the original inputs and their associations, the model may infer unknown relationships from anonymous data, allowing it to generalize and predict unknown data. • ANN does not impose any constraints on the input variables, unlike many other prediction approaches (like how they should be distributed). This is particularly helpful in financial time series forecasting (for example, stock prices) when significant data volatility.
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  Application of ArtificialNeural Networks: ANNs have a wide range of applications because of their unique properties. Image Processing and Character recognition: ANNs play a significant part in picture and character recognition because of their capacity to take in many inputs, process them, and infer hidden and complicated, non-linear correlations. Character recognition, such as handwriting recognition, has many applications in fraud detection (for example, bank fraud) and even national security assessments.
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  Forecasting: Forecasting is widelyused in everyday company decisions (sales, the financial allocation between goods, and capacity utilization), economic and monetary policy, finance, and the stock market. Forecasting issues are frequently complex; for example, predicting stock prices is complicated with many underlying variables (some known, some unseen).
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  Advantages of ArtificialNeural Network The advantages of the neural network are as follows − •A neural network can implement tasks that a linear program cannot. •When an item of the neural network declines, it can continue without some issues by its parallel features. •A neural network determines and does not require to be reprogrammed. •It can be executed in any application. Disadvantages of Artificial Neural Network The disadvantages of the neural network are as follows − •The neural network required training to operate. •The structure of a neural network is disparate from the structure of microprocessors therefore required to be emulated. •It needed high processing time for big neural networks.
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  There are severaldifferent architectures for ANNs, • Feedforward Neural Networks • Recurrent Neural Networks (RNNs) • Convolutional Neural Networks (CNNs) • Generative Adversarial Networks (GANs)