Neural Networks #

• A network of artificial neurons inspired by how neurons function in the human brain.
• At its core - a mathematical model designed to process and learn from data.
• Neural networks form the foundation of Deep Learning (involves training large and complex networks on vast amounts of data).

flowchart LR
 subgraph subGraph0["Input Layer"]
        I1(("Input 1"))
        I2(("Input 2"))
        I3(("Input 3"))
  end
 subgraph subGraph1["Hidden Layer"]
        H1(("Hidden 1"))
        H2(("Hidden 2"))
        H3(("Hidden 3"))
  end
 subgraph subGraph2["Output Layer"]
        O(("Output"))
  end
    I1 --> H1 & H2 & H3
    I2 --> H1 & H2 & H3
    I3 --> H1 & H2 & H3
    H1 --> O
    H2 --> O
    H3 --> O

    style I1 fill:#C8E6C9
    style I2 fill:#C8E6C9
    style I3 fill:#C8E6C9
    style H1 stroke:#2962FF,fill:#BBDEFB
    style H2 fill:#BBDEFB
    style H3 fill:#BBDEFB
    style O fill:#FFCDD2
    style subGraph0 stroke:none,fill:transparent
    style subGraph1 stroke:none,fill:transparent
    style subGraph2 stroke:none,fill:transparent

Structure of a Neural Network #

A typical neural network has three main layers:

1. Input Layer

   • Receives the data (features) and passes it to the hidden layer.
   • Each neuron in this layer represents one input variable.

2. Hidden Layer(s)

   • The core computational part where the actual learning happens.
   • Each input is multiplied by a weight, a bias is added, and the result is passed through an activation function.
   • The activation function decides whether a neuron should be activated (i.e. contributes to the final output).
   • There can be one or multiple hidden layers depending on network complexity.

3. Output Layer

   • Produces the final prediction or classification result.
   • The output can be a single value (for regression) or multiple probabilities (for classification).

Training #

• During training, the network adjusts weights and biases
• Uses algorithms like backpropagation to minimise prediction error.
• Goal to make the predicted output as close as possible to the actual (labelled) output.

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