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
Convolutional Neural Networks
This course is part of Deep Learning Specialization
Instructors: Andrew Ng
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Gain insight into a topic and learn the fundamentals.
4.9
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Intermediate level
Recommended experience
Flexible schedule
Approx. 35 hours
Learn at your own pace
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Assessments
4 assignments
Taught in English
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This course is part of the Deep Learning Specialization
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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.
What's included
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.
What's included
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!
What's included
11 videos6 readings1 assignment2 programming assignments
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4.9 (3,183 ratings)Offered by
Recommended if you're interested in Machine Learning
University of Colorado Boulder
Google Cloud
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Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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