Coursera: Convolutional Neural Networks

 with  Andrew Ng
This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.

You will:
- Understand how to build a convolutional neural network, including recent variations such as residual networks.
- Know how to apply convolutional networks to visual detection and recognition tasks.
- Know to use neural style transfer to generate art.
- Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.

This is the fourth course of the Deep Learning Specialization.


Foundations of Convolutional Neural Networks
Learn to implement the foundational layers of CNNs (pooling, convolutions) and to stack them properly in a deep network to solve multi-class image classification problems.

Deep convolutional models: case studies
Learn about the practical tricks and methods used in deep CNNs straight from the research papers.

Object detection
Learn how to apply your knowledge of CNNs to one of the toughest but hottest field of computer vision: Object detection.

Special applications: Face recognition & Neural style transfer
Discover how CNNs can be applied to multiple fields, including art generation and face recognition. Implement your own algorithm to generate art and recognize faces!

3 Student
Cost Free Online Course (Audit)
Pace Upcoming
Provider Coursera
Language English
Certificates Paid Certificate Available
Calendar 4 weeks long
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Reviews for Coursera's Convolutional Neural Networks
4.7 Based on 3 reviews

  • 5 stars 67%
  • 4 star 33%
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5.0 a month ago
Rafael Espericueta completed this course.
This course was one of the best courses I've ever taken - but one can say the same for any of Andrew Ng's courses! You're not just learning about cutting edge computer vision techniques, carefully and thoroughly explained, you're gleaning the distilled wisdom of a true master of deep learning. Even one of these wisdom gems he dispenses so freely throughout his courses could have saved some DL team months of wasted work. I really can't recommend this course highly enough (and the same goes for the entire Deep Learning Specialization).
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5.0 2 months ago
Anonymous completed this course.
Prof. Andrew Ng covers the topics in quite enough detail and explains the concepts very properly. I was really blown away by his lectures on YOLO and inception net. This course is highly recommended for anyone who has some understanding of fully connected neural nets and wants to learn about CNNs. The only issue I think about this course is that the programming assignments do a lot of babysitting, making them very easy which is good for beginners but not for the intermediate students.
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4.0 2 months ago
by Y. Nicodeme completed this course, spending 5 hours a week on it and found the course difficulty to be medium.
A nice course on convolutional neural networks, face recognition and neural style transfer. it contains tensorflow and keras hands-on examples
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