In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.
Convolutional Neural Networks
This course is part of Deep Learning Specialization
Instructors: Andrew Ng
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There are 4 modules in this course
Implement the foundational layers of CNNs (pooling, convolutions) and stack them properly in a deep network to solve multi-class image classification problems.
What's included
12 videos6 readings1 assignment2 programming assignments
Discover some powerful practical tricks and methods used in deep CNNs, straight from the research papers, then apply transfer learning to your own deep CNN.
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14 videos3 readings1 assignment2 programming assignments
Apply your new knowledge of CNNs to one of the hottest (and most challenging!) fields in computer vision: object detection.
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14 videos4 readings1 assignment2 programming assignments
Explore how CNNs can be applied to multiple fields, including art generation and face recognition, then implement your own algorithm to generate art and recognize faces!
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11 videos6 readings1 assignment2 programming assignments
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Johns Hopkins University
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Reviewed on Aug 5, 2019
Great content in lectures! Automatic graders for programming assignments can be tricky, and set to old versions of tf sometimes, but answers to these issues are readily found in the discussion forums.
Reviewed on Jan 11, 2019
Amazing! Feels like AI is getting tamed in my hands. Course lectures , assignments are excellent. To those who are not well versed with python - numpy and tensorflow , it would be better to brush up.
Reviewed on Sep 3, 2020
Great course. Easy to understand and with very synthetized information on the most relevant topics, even though some videos repeat information due to wrong edition, everything is still understandable.
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