OER·harvester

← Back to the library
Zenodo PDF resource

Foundations of Artificial Intelligence & Machine Learning

Licence
OPEN CC-BY-4.0
Authors
Nidhi Sharma, Honey Singh, Ajay Sharma, Deepak Dagar
Published
2026-07-28 · Zenodo
Language
eng
Length
37166 words
Type
narrative text
Open ↗ Download Open original ↗
Hidden Layers

Hidden layers perform most of the computational work in neural networks. These layers analyze relationships, detect patterns, and transform information into more meaningful representations. A network may contain:

  • one hidden layer

  • multiple hidden layers

  • hundreds of layers in advanced deep learning systems The greater the number of hidden layers, the deeper the network becomes. Output Layer The output layer produces the final prediction or classification result. For example:

  • predicting a disease category

  • identifying an object in an image

  • generating a translated sentence The structure of the output layer depends on the type of problem being solved.

Figure 7.2: Structure of a Neural Network

The figure represents the basic architecture of a neural network consisting of interconnected input, hidden, and output layers responsible for data processing and prediction generation. Information Flow in Neural Networks Neural networks process information layer by layer. The flow generally follows this sequence:

  • input data enters the network
  • hidden layers process the information
  • mathematical operations transform the data
  • outputs are generated This process is called forward propagation.

Initially, predictions may be inaccurate because weights are random. During training, the network gradually improves by adjusting weights based on prediction errors. Learning Process in Neural Networks Neural networks learn through repeated training cycles. During each cycle:

  • predictions are generated

  • errors are calculated

  • weights are adjusted

  • performance improves gradually This optimization process allows the network to recognize increasingly complex patterns. The learning process depends heavily on:

  • training data quality

  • network structure

  • optimization algorithms

  • computational resources