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ARTIFICIAL NEURAL NETWORK (ANN)

ARTIFICIAL NEURAL NETWORK (ANN). by Dhanik Patel & Yong Kok Khuen. LITERATURE REVIEW. Fundamentals of Neural Networks: Architectures, Algorithms and Applications by Laurene V. Fausett Obtained the definition of ANN Reviewed on different types of ANN architectures and learning methods

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ARTIFICIAL NEURAL NETWORK (ANN)

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  1. ARTIFICIAL NEURAL NETWORK (ANN) by Dhanik Patel & Yong KokKhuen

  2. LITERATURE REVIEW Fundamentals of Neural Networks: Architectures, Algorithms and Applications by Laurene V. Fausett Obtained the definition of ANN Reviewed on different types of ANN architectures and learning methods Learnt some examples of application areas of ANN

  3. INTRODUCTION What is ANN? An information processing system that has certain performance characteristics that is similar to a biological neural network

  4. INTRODUCTION Analogy Biological NN ANN

  5. INTRODUCTION Analogy

  6. INTRODUCTION Brief history of ANN

  7. INTRODUCTION Examples of application areas of ANN - Signal Processing - Control - Pattern Recognition - Medicine - Speech Production - Speech Recognition

  8. CORE IDEA How ANN works? A simple neural network x1 w1 x2 w2 Y w3 x3

  9. CORE IDEA How ANN works? Neuron Y receives inputs from neurons X1, X2, and X3 The output signals of these neurons are x1, x2, and x3 The weights on the connections to neuron Y are w1, w2, and w3 The net input y_in to neuron Y is the sum of the weighted signals y_in = w1x1 + w2x2 + w3x3

  10. CORE IDEA Some typical architectures Single layer networks Multi-layer networks Recurrent networks

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