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ANN - Multi-Layer Perceptron (MLP) with Two Hidden Layers

  UNIT – IV   1. Multi-Layer Perceptron (MLP) with Two Hidden Layers A Multi-Layer Perceptron with two hidden layers is a feed-forward artificial neural network that consists of: One input layer Two hidden layers One output layer It is capable of learning complex non-linear relationships in data.   Architecture of MLP with Two Hidden Layers Layers: Input Layer Receives input features No computation is performed Hidden Layer 1 Receives input from input layer Performs weighted summation and activation Hidden Layer 2 Receives output from hidden layer 1 Further extracts features Output Layer Produces final output Each neuron in a layer is connected to all neurons in the next layer.   Working of MLP with Two Hidden Layers Activation Functions Used Hidden layers : ReLU / Tanh / Sigmoid Output layer : Sigmoid (binary classifica...
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Data Structure - Unit I - ABSTRACT DATA TYPES (ADT)

  ABSTRACT DATA TYPES (ADT) AND LISTS 1. ABSTRACT DATA TYPES (ADT) An Abstract Data Type (ADT) is a logical specification of a data type which defines: The data objects The operations that can be performed on those objects ADT does not specify the implementation details . It only describes what operations are allowed , not how they are carried out .   Need for ADT To separate logic from implementation To improve software reliability To support data abstraction   Components of ADT Data – Values stored Operations – Actions on data Interface – Interaction with user/program   ADT Operations : Create() Insert(position, element) Delete(position) Search(element) Traverse()   Advantages of ADT Implementation independent Easier debugging Improves modularity Supports code reusability   2. LIST ADT A List ADT is a linear collection of elements arranged in a ...
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