Course 4 - Convolution Neural Networks

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Last Updated: Tuesday, 15 December 2020 08:35

Published: Tuesday, 15 December 2020 08:27

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Course 4 - Convolution Neural Networks

This course is one of the most important course on NN, as convolution NN (CNN) is the one that is used most of the places in computer vision. Computer vision is the discipline of CS where we extract features in any given picture or video, i.e in autonomous cars, it's the art of extracting features from picture such as other cars, pedestrians, etc. CNN has been very successful in Computer vision.

There are 4 sections in this course:

1. Foundations of CNN: This is a lengthy course (2 hrs) as it talks about basics of CNN (conv layers and pooling layers). It explains this with some basic examples. Spend some time on this course. Go over it again so that you get the basics. There are 2 programming assignments

2. Deep Convolutional models: case studies: It has 1 pgm assgn on Residual NN (RNN)

3. Object detection: It has 1 pgm assgn on "car detection with YOLO"

4. Special applications: Facial recognition and neural style transfer: This has 2 pgm assgn. 1st one is "art generation with neural style transfer", while 2nd one is on "facial recognition"