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Deep Learning Theoretical Course (Course VII)

Deep Learning Theoretical Course (Course VII)

Deep Learning Theoretical Course (Course VII)

Welcome to this course on Deep Learning!

In this course, you will be learning the theoretical concepts behind building a Deep Neural Network (more specifically, a Dense Neural Network) and why they are widely used nowadays in building state-of-the-art Machine Learning and Artificial Intelligence solutions.

Objectives of the course

The learning objectives of the course are set out as follows:

  • Learn the fundamental operations of a Deep Neural Network
  • Learn the theory behind building a Deep Neural Network architecture
  • Learn about back-propagation and gradient descent

You can expect to have all of these objectives met by the time you reach the end of this course.

Pre-requisites for the course

This is a fairly advance course and requires a good amount of knowledge in Deep Learning. Therefore, the following pre-requisites are required for you to get the best out of the course:

  • Solid understanding of Machine Learning
  • Solid understanding of Linear Algebra, Calculus, Probability and Statistics, Numerical Computation and Information Theory

If you do not satisfy the above pre-requisites, don’t worry! You can always come back later to this course once you are ready.

Ready to add a fundamental skill to your data science portfolio? See you in the course!

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Kharpann was founded by a team of mathematicians, programmers and machine learning/artificial engineers with a vision to help businesses find their data science team faster and to help them grow with their own data.
Price Free
Instructor Kharpann
Duration 2 weeks
Lectures 8
Enrolled 1 student
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