Deep Learning with Tensorflow

No prerequisites

1. Understand the difference between linear and non-linear regression
2. Comprehend Convolutional Neural Networks and their applications
3. Gain familiarity on Recurrent Neural Networks (RNN) and Autoencoders
4. Learn how to filter with Restricted Boltzmann Machine

1. Lesson 1 - Introduction to TensorFlow

2. Lesson 2 – Convolutional Neural Networks (CNN)

3. Lesson 3 – Recurrent Neural Networks (RNN)

4. Lesson 4 - Unsupervised Learning

5. Lesson 5 - Autoencoders

The online delivery blends synchronous and asynchronous components. Students complete self-directed assignments hosted on the course platform. Weekly Forums support a required active, rubric-based student contributions that foster collaboration.

Exams may include essays, short answers, or MCQs. Formats include open/closed book. Time zones are considered. Assessments are formative and summative. Students have 2 weeks to review grades and must follow the appeal process in the Student Handbook.

Certificate

The EBU ensures administrative and Tech support in an inclusive, accessible learning environment for all students, including those with disabilities or special needs. This policy applies to all students, staff, and learning spaces, full-or part-time.

Please note that there are three semesters as intake periods as noted on the academic calendar.
Scholarship Awarded (Total Fee: €100)

Registration is done online.