Compare courses
Register
Nanyang Technological University Center for Continuing Education

Neural Networks and Deep Learning

This course has no confirmed dates in the future. Subscribe to be notified when it is offered.

Relevant courses

Course format
Starting after
Ending before

Disclaimer

Coursalytics is an independent platform to find, compare, and book executive courses. Coursalytics is not endorsed by, sponsored by, or otherwise affiliated with Nanyang Technological University Center for Continuing Education.

Full disclaimer.

Description

Artificial neural networks are models of reasoning based on human brain functioning and have been successful in many real-world applications including pattern classification, regression, forecasting, etc. The course will introduce models, learning, implementations and applications of neural networks and deep learning.

Objectives

To equip participants with the basic concepts and methodologies of neural networks and deep learning systems. In particular, this course covers the information processing techniques inspired by the workings of biological neural networks, which provides solution to interrogatives that current linear systems are not able to resolve. Basic neuron models, neural layers, feedforward networks, convolutional neural networks, autoencoders, and recurrent neural networks will be covered in the course. Students will be given hands-on experience in building neural network models, using Python and Tensorflow libraries. After taking this course, from shallow to deep neural networks, students will be able to design and select suitable neural network model for solving real world applications and perform required simulations and implementations.

Outline

Day 1:

  • Introduction to neural networks
  • Pattern recognition
  • Regression
  • Implementing neural networks, using Python and Theano

Day 2:

  • Neural layers
  • Feedforward neural networks
  • Model selection and overfitting

Day 3:

  • Convolutional neural networks
  • Recurrent neural networks
  • Gated RNN
  • Autoencoders

Who should attend

Technicians, engineers, data modelers, and computational scientists who are interested in developing neural network models to solve computational problems, including pattern/object recognition, regression, prediction, forecasting, etc. Knowledge of linear algebra, calculus, basic programming skills, and Python would be useful.

Experts

Jagath Rajapakse is Professor of Computer Engineering at the Nanyang Technological University, Singapore. He obtained his Ph.D. degree in electrical and computer engineering from the University of Buffalo, USA. He had been a Visiting Professor to Massachusetts Institute of Technology (MIT), USA, ...
Show more