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                        Neural Networks

Neural networks are computing systems inspired by the human brain, consisting of layers of interconnected nodes (neurons). They process data by learning patterns from input, enabling tasks like image recognition, language translation, and decision-making. Neural networks power many AI applications by adjusting internal weights through training with large datasets.

                                                  


Structure of a Neural Network

  1. Input Layer:
    This is where the network receives data. Each neuron in this layer represents a feature in the dataset (e.g., pixels in an image or values in a spreadsheet).

  2. Hidden Layers:
    These layers sit between the input and output layers. They perform calculations and learn patterns. The more hidden layers a network has, the deeper it is—hence the term deep learning.

  3. Output Layer:
    This layer provides the final result, such as classifying an image or predicting a number.

How It Works

Each connection between neurons has a weight, and each neuron applies an activation function to decide whether to pass the signal forward. During training:

  • The network receives data (inputs).

  • It makes a prediction (outputs).

  • The prediction is compared to the actual answer using a loss function.

  • The error is reduced by adjusting the weights using a method called backpropagation and an optimization algorithm like gradient descent.

Applications

Neural networks are used in a wide range of fields:

  • Image and speech recognition

  • Natural language processing (NLP)

  • Autonomous vehicles

  • Medical diagnosis

  • Financial forecasting

  • Recommendation systems (like those used by Netflix or Amazon)

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