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The course is not from a very prestigious university.

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This course costs very less.

Difficulty

66% of the students have found this course difficult.

Content

80% of the students have liked the content of this course.

Assignments

64% of the students have liked the assignments of this course.

Teaching

86% of the students have liked how the instructor has taught this course.

Satisfaction

82% of the students are overall satisfied with this course.

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Convolutional Neural Networks

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21 hours
English
DeepLearning.AI
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CourseraCourses from Coursera
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For Rs. 2182
Intermediate
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Checklist

Certification

You will get a certificate on completing this course.

University

The course is not from a very prestigious university.

Price

This course costs very less.

Difficulty

66% of the students have found this course difficult.

Content

80% of the students have liked the content of this course.

Assignments

64% of the students have liked the assignments of this course.

Teaching

86% of the students have liked how the instructor has taught this course.

Satisfaction

82% of the students are overall satisfied with this course.

Edvicer's Rewards

You can get a cashback of ₹ 100 on buying this course.

Why should you choose this course?

Description

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.

Syllabus

Foundations of Convolutional Neural Networks
Deep convolutional models: case studies
Object detection
Special applications: Face recognition & Neural style transfer

What others say about this course

Reviews from Coursera

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.

Dear Instructors, This is most frustrating course in all of your courses so far. The instructions were completely misguiding the candidates from YOLO implementation onwards. All along you presented the  Read More ...

This course is definitely tougher than the first three courses. Challenging but worth it.

Great course for kickoff into the world of CNN's. Gives a nice overview of existing architectures and certain applications of CNN's as well as giving some solid background in how they work internally.

Great lectures but the programming assignments feel as if it is testing your proficiency with tensorflow which is neither formally covered in the lecture nor the most intuitive framework to understand   Read More ...

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