In this course, we will be creating an end-to-end application that can detect smiles in images and videos.

For that, we will use deep learning and start by training a convolutional neural network on the SMILES dataset which contains faces of people smiling and not smiling. Once the network is trained, we will go through the following steps to detect smiles in images and videos:

1. We will use Haar cascades to detect a face in an image.

2. We will then extract the face region from the image.

3. then we will pass the face region to the network for classification.

4. And finally, we will annotate the image with the label "smiling" or "not smiling" depending on the output of the network.

This class was made for intermediate Python programmers that have some familiarity with deep learning and computer vision.

By the end of this course, you'll have a fully functional smile detection application that you can use in your own projects.

What you'll get:

When you buy the course you'll receive:

• 1 hour of HD video tutorials

• Source code, example images, and videos used in the course

• Lifetime access to the course

• Priority support

What is covered in this course:

Just so that you have some idea of what you will learn in this course, these are the topics that we will cover:

• Load the Data

• Train the Smile Detector

• Apply Our Smile Detector to Images

• Apply Our Smile Detector to Videos