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Convolutional Neural Network Theoretical Course (Course VIII)

Convolutional Neural Network Theoretical Course (Course VIII)

Convolutional Neural Network Theoretical Course (Course VIII)

Welcome to this course on Convolutional Neural Networks!

In this course, you will be learning the theoretical concepts behind building a Convolutional Neural Network and why they are widely used nowadays in image classification problems.

Objectives of the course

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

  • Learn the fundamental operations of a Convolutional Neural Network
  • Learn the theory behind building a Convolutional Neural Network architecture
  • Learn how to calculate the output tensor size for different layers of a Convolutional Neural Network

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 Dense Neural Networks
  • Solid understanding of back-propagation, gradient descent and deep learning in general

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 10
Enrolled 628 students
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