Nullam dignissim, ante scelerisque the is euismod fermentum odio sem semper the is erat, a feugiat leo urna eget eros. Duis Aenean a imperdiet risus.
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Nullam dignissim, ante scelerisque the is euismod fermentum odio sem semper the is erat, a feugiat leo urna eget eros. Duis Aenean a imperdiet risus.
Neural networks mimic the way the human brain processes information. At their core, they consist of layers of nodes (neurons) connected by weights. Data flows through these layers, and each neuron applies a mathematical function to transform the input. The output from one layer becomes the input for the next.
Key components include:
Input Layer: Receives raw data (e.g., images, text).
Hidden Layers: Process and extract patterns using weights and biases.
Output Layer: Produces predictions or classifications.
Neural networks are trained using a process called backpropagation, where the model learns by minimizing error through repeated adjustments of weights.