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A classification neural network is a supervised learning model designed to assign input data to one of several predefined categories. It consists of layers of interconnected neurons — an input layer, one or more hidden layers, and an output layer — with weights and activation functions determining how data flows and transforms through the network .
Key components include:
Input layer: Receives feature vectors.
Hidden layers: Apply weighted sums and non-linear activations (e.g., Sigmoid, ReLU, Tanh) to learn complex patterns.
Output layer: Uses Softmax for multi-class or Sigmoid for binary classification to produce probabilities .
Loss function: Typically Cross-Entropy for classification.
Optimization: Uses backpropagation with algorithms like SGD, Adam, or L-BFGS to minimize loss.
Example: Multi-class classification with scikit-learn
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