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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 ↗
Training of Neural Networks

Training is the process through which neural networks learn from data. Initially, network predictions are usually inaccurate because weights are assigned randomly. During training, the system repeatedly adjusts these weights to reduce prediction errors. The training process generally involves:

  • forward propagation
  • error calculation
  • weight adjustment
  • repeated learning cycles As training continues, the model gradually improves prediction accuracy. Backpropagation Backpropagation is one of the most important learning mechanisms in neural networks. After generating predictions, the network calculates errors and sends this information backward through the layers. The system then updates weights to reduce future mistakes. This repeated correction process allows neural networks to learn efficiently from data.

Figure 7.4: Backpropagation Process

The figure illustrates how prediction errors move backward through neural network layers during training to improve weights and model performance. Epochs and Learning Rate An epoch represents one complete training cycle through the dataset. Neural networks usually require multiple epochs to learn properly. Another important factor is the learning rate, which controls how much weights change during training. A very high learning rate may make training unstable, while a very low learning rate can slow the learning process. Proper training settings help neural networks achieve better accuracy and stability. Importance in Modern AI Activation functions and training methods form the foundation of modern deep learning systems. Applications such as:

  • speech assistants
  • facial recognition
  • recommendation systems
  • autonomous vehicles depend heavily on efficient neural network training and optimization techniques. These mechanisms enable AI systems to continuously improve and handle highly complex real-world tasks.

7.3 Introduction to Deep Learning

Deep Learning is an advanced branch of Machine Learning that uses neural networks with multiple hidden layers to learn complex patterns from large datasets. The term “deep” refers to the presence of many processing layers inside the network. Traditional Machine Learning algorithms often require manual feature extraction, but deep learning systems can automatically learn features directly from raw data. This ability has made deep learning highly successful in modern Artificial Intelligence applications. Deep learning became popular due to:

  • increased computing power
  • availability of large datasets
  • improvements in neural network training Today, many advanced AI systems rely on deep learning models.