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neural network

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This document provides an introduction to neural networks, including their basic components and types. It discusses neurons, activation functions, different types of neural networks based on connection type, topology, and learning methods. It also covers applications of neural networks in areas like pattern recognition and control systems. Neural networks have advantages like the ability to learn from experience and handle incomplete information, but also disadvantages like the need for training and high processing times for large networks. In conclusion, neural networks can provide more human-like artificial intelligence by taking approximation and hard-coded reactions out of AI design, though they still require fine-tuning.

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Neural Network Guided by Chapala Maharana Submitted By- Nilmani Singh Branch-CSE Regd. No.-0601289251
Contents .Introduction to Neural Network .Neurons .Activation Function .Types of Neural Network .Learning In Neural Networks .Application of Neural Network .Advantages of Neural Network .Disadvantages Of Neural Network
Neural Networks ,[object Object]
A powerful technique to solve many real world problems.
The ability to learn from experience in order to improve their performance.
Ability to deal with incomplete information,[object Object]
Developed in 1943
Comprised of one or more layers of neurons
Several types, we’ll focus on feed-forward and feedback networks,[object Object]
Neural Network Neurons Receives n-inputs Multiplies each input by its weight Applies activation function to the sum of results Outputs result
Activation Functions Controls when unit is “active” or “inactive” Threshold function outputs 1 when input is positive and 0 otherwise Sigmoid function = 1 / (1 + e-x)
Types of Neural Networks Neural Network types can be classified based on following attributes: ,[object Object], - Static (feedforward) - Dynamic (feedback) ,[object Object], - Single layer - Multilayer - Recurrent ,[object Object], - Supervised - Unsupervised - Reinforcement
Classification Based On Connection Types ,[object Object]
Dynamic(Feedback)Classification Based On Topology ,[object Object]
Single layer
Multilayer(unit delay operator z-1implies dynamic system)
Classification Based On Learning Method Supervised Unsupervised Reinforcement Supervised learning ,[object Object]
At each presentation: adapt weights
After many epochs convergence to a local minimum,[object Object]

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neural network

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