Artificial Neural Networks Training in Vizag | Softenant Technologies

Artificial Neural Networks explains the learning mechanics behind connected layers of artificial neurons. Learners work from input features and weights to activations, loss functions, backpropagation, optimisation and model evaluation.

Technology-specific learning focus

The course makes ANN decisions visible: data splits, learning-rate changes, overfitting signals, confusion matrices and the difference between a baseline model and a neural approach.

Skills and syllabus areas

  • Python data workflow concepts
  • Perceptrons, layers and activation functions
  • Loss, gradients and backpropagation
  • Train/validation/test design
  • Regularisation and error analysis

Project evidence

Binary classification ANN experiment
Customer-churn prediction baseline comparison
Handwritten-digit classification study
Learning-curve and error-analysis report

How practical work is assessed

Projects are reviewed against defined inputs, implementation choices, test cases, output quality and documented limitations. Learners should be able to explain the workflow, not just show a final screen or code sample.