Artificial Neural Networks (ANN)

Artificial Neural Networks are Machine Learning algorithm based on the concept of a human neuron. They receive data as inputs, then the neurons consolidate all the information, then the output is generated.

Training Process:

  1. First Phase: The first phase consists of applying a nonlinear transformation of the input and create a statistical model as output.
  2. Second Phase: The second phase aims at improving the model with a mathematical method known as derivative.
  3. This process is repeated in a number of epochs(iterations) until it has reached a tolerable level of accuracy.

First a neural network is trained with a given dataset and optimized through testing and evaluation. Then the model and it's algorithm is used as a software function to perform task.


  • Neural Network Layers:
    • Input Layer: The first layer of a neural network that receives the data as input.
    • Hidden Layer: This layer is responsible for the calculations performed on features(giveb as input data).
    • Output Layer: Last layer of a neural network that provides the final output.
  • Perceptron
  • Weights
  • Activation Functions
  • Bias


Classification of Neural Networks:

  • Shallow neural network: The Shallow neural network has only one hidden layer between the input and output.
  • Deep Neural Networks: Deep Learning

Regularization techniques for ANNs:

Optimization Techniques:

Limitations of ANNs:

Goals of ANN Optimization:

  • Faster performance
  • Reduced computational requirements
  • Optimized space usage