Deep learning neural networks are generally easy to define and fit, however, they are still hard to configure for optimum performance. There are no hard and fast rules to optimise a network for a given problem and it is not possible to analytically calculate the optimal model type or model configuration for a given dataset. In this course, participants will work through techniques that improve model learning in response to a training dataset, reduced overfitting and improve prediction.
We recommend participants to complete Deep Learning with Python before signing up for this course.
Laptops will be provided for the duration of the course.
Learning Objectives
By the end of the course, participants will be able to:
- Develop framework for deep learning optimization
- Apply techniques for optimising learning, optimising generalisation and improving prediction
Who should attend?
App developers, AI/ML engineers, Data Engineers
Entry Requirement
Participants should have a good understanding and knowledge of Machine Learning Fundamentals and Deep Learning
Certification
Participants will be awarded a certificate of completion upon meeting the 75% course attendance requirement.
For courses with assessment component, participants will be awarded the certification of completion upon passing the assessment. Otherwise, a certification of attendance will be issued instead upon meeting the 75% course attendance requirement.
Please click on the "Register" button to view the updated course schedule and fees on the Skills Training & Enhancement Portal (STEP).
Please click on the "Register" button to view the updated course schedule and fees on the Skills Training & Enhancement Portal (STEP).