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Introduction – Deep Learning Theoretical Course

Deep Learning Theoretical Course

Introduction – Deep Learning Theoretical Course

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 (Supervised 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.

Best way to work through the course

The course is long and requires a good amount of attention from your end.

Before moving to the next lecture, we suggest you to set up your coding environment and open up your Jupyter Notebook. If you are a more advanced user of Python and have your own preferences, please feel free to choose an IDE that you prefer. However, all of the coding examples will be written for execution on Jupyter Notebook cells.

If you come across any problem, please check to see if your code matches exactly with the course or not. If you still are facing errors or have some doubts, please provide your question through the comment section of the specific chapter you are stuck on.

We also recommend you join our community and get connected to our vibrant network of data science aspirants. Once you are in the community, you can share your learnings, form a study group, or even get help building a project around Deep Learning.

Ready to start your Deep Learning journey? Let us head on to the course and start learning.

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