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Convolutional Neural Networks,

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Рецензии: 2,511

Об этом курсе

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....

Лучшие рецензии

автор: AG

Jan 13, 2019

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.

автор: EB

Nov 03, 2017

Wonderful course. Covers a wide array of immediately appealing subjects: from object detection to face recognition to neural style transfer, intuitively motivate relevant models like YOLO and ResNet.

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Рецензии: 2,477

автор: Jayant Rane

Apr 20, 2019

Learned a lot!!

автор: Sherif Mehralivand

Apr 19, 2019

Again a great course by Andrew Ng and his great team. Convolutional neural networks are the reason for the recent Deep Learning revolution or let's say better renaissance. Andrew does a great job in explaining the theory, math and application fields of CNNs while also telling about the history of recent advances in CNN algorithms and architectures.

Great job!

автор: Patty

Apr 19, 2019

Nice courses. The programming assignments are quite good for me to get knowledge quickly.

автор: Yash Nigam

Apr 19, 2019

Super challenging and Super fun course! Totally recommend it!

автор: Huijun Park

Apr 18, 2019

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 so you'll spend so much time digging through convoluted tensorflow documents and qna and whatnot to debug your codes that you would rather learn tensorflow formally first and then take this course and still end up finishing it faster than only going through this course only but it is only the programming assignments that basically assume that you are already familiar with the tensorflow framework so if you are only going to go over the video lectures it gives a great overview of how CNN works and many useful algorithms which can applied to a assortment of situations

автор: eren atmaca

Apr 16, 2019

pretty helpful for the begineers

автор: Yan

Apr 15, 2019

I was always curious about the "CNN" concept every time it emerged in the news. Thanks to Prof. Andrew's mild explanation, now I get a straight intuition into it!

The assignments were very amusing in this section. It was not hard to get a pass with the help of forums, but understanding every step is more important I think. So I will come back to practice more.

автор: Saumya Tiwari

Apr 15, 2019

Good Explanation.

автор: Sebastian Javier Marchano

Apr 15, 2019


автор: Akash Singh

Apr 14, 2019

had a lot of fun in this course, i would, recommend every student to take up this course as it gave me an insight on how human eyes process images. this also helped me a lot to understand deep learning better