Convolutional Neural Network Theoretical Course (Course VIII)July 17, 2020 2020-08-04 10:54
Convolutional Neural Network Theoretical Course (Course VIII)
- Welcome to Course VIII!
- Introduction to Convolutional Neural Networks
- The Convolution Operation
- Stride and Calculation of Output Size
- The Pooling Operation
- The Convolution/Pooling Operation for RGB images
- Padding an Image
- Building a Convolutional Neural Network
- Training a Convolutional Neural Network
- End of Course
Welcome to 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 as well as text 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.
Best way to work through the course
The course is not long but 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 Convolutional Neural Networks.
Ready? Let us head on to the course and start learning.