Deep Learning Principles & Applications

Deep learning is a widely and immediately applicable AI tool that is actively used in several disciplines across engineering, natural sciences, health care, finance, and humanities. This workshop aims to provide the participants a hands-on computation-based introduction to the principles and applications of deep learning. Leveraging on a quick, yet accessible introduction to key ideas from linear algebra, participants will be introduced to building a simple neural network from scratch and understanding the fundamental operating principle—the “learning” process. Seamless extension to deep neural networks will follow with multidisciplinary computational examples.

Learning Outcomes

Course Content

  • Essential linear algebra for deep learning
  • Fundamentals of linear classification: weights, bias, scores, and loss functions
  • Calculus for the gradient descent algorithm
  • Forward and backward propagation with regularization
  • Batch processing for large datasets
  • Linear to nonlinear classification via activation functions
  • Computational setup of a shallow neural network
  • Tuning neural network performance
  • Pre-processing data and batch normalization
  • Cross-validation for validating model performance
  • Extending the computational setup from a shallow to a deep neural network
  • Introduction to the TensorFlow library
  • Application projects: implementing shallow and deep neural network models using TensorFlow; implementing machine learning models on edge devices using Edge Impulse.


Name of the Coordinators

Prof . Sudarsan N S Acharya
Dr . Raghavendra Prabhu
Mr . Srikanth Shenoy

Distinctive Features

Offered by

Manipal School of Information Sciences, MAHE