3.5 Neural Network & Bio-inspired Multi-Layer Networks in machine learning

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Neural Network: -

  • A Neural Network is a bio-inspired machine learning models that mimic from working of  the human brain.
  • The human brain has millions of neurons that send signals to each other. Similarly, a neural network has artificial neurons (nodes) that process information and learn patterns from data. Because it is inspired by the biological brain, it is called a bio-inspired model.
  • The neural network structure consist of input layers, hidden layers, and output layers that process information and learn patterns from data. Because they contain multiple layers of neurons, they are called multi-layer neural networks.
  •  The input layer receives the data.

            Example inputs:

                                        Age
                                        Study hours
                                        Attendance
                                        Marks

              Each input value is given to a neuron.

  •  The hidden layer processes the information. It performs calculations and learns patterns in the data. A neural network can have one or many hidden layers. More hidden layers help the model learn complex relationships.


  • The output layer gives the final prediction.

            Example outputs:

  • Spam / Not Spam
    Pass / Fail
    Cat / Dog
    Price prediction

  • They are widely used in applications like image recognition, speech processing, and prediction systems.


  • A neural network learns by receiving input, processing it through multiple layers, and producing an output. 
  • Each layer helps the model understand the data better.

            For example:

                                                Input → Processing → Output

            Example:    Student marks → Neural network → Pass / Fail prediction

Example

Neural networks are widely used in image recognition.

Input → Image of an animal

The network processes many features such as:

  • Shape

  • Color

  • Size

Output:

Cat or Dog


Advantages of Neural Networks

  • Can learn complex patterns

  • Works well with large data

  • Used in image, speech, and text processing

  • Can improve accuracy over time


Applications of Neural Networks

Neural networks are used in many real-world applications such as:

  • Image recognition

  • Speech recognition

  • Self-driving cars

  • Medical diagnosis

  • Recommendation systems












































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