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.
- 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!
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.
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 thin
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.