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Avis et commentaires pour l'étudiant pour Convolutional Neural Networks par deeplearning.ai

4.9
24,923 notes
3,005 avis

À propos du cours

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....

Meilleurs avis

RK

Sep 02, 2019

This is very intensive and wonderful course on CNN. No other course in the MOOC world can be compared to this course's capability of simplifying complex concepts and visualizing them to get intuition.

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.

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126 - 150 sur 2,966 Examens pour Convolutional Neural Networks

par David T

Feb 17, 2019

This is a very meaty course. Lots of hands on exercise, and the material and techniques taught are very recent. Thank you Andrew!

par Zian Y

Feb 18, 2019

The assignment system is horrible for the last homework. Everything else is good.

par Gregory R G J

Feb 18, 2019

Thumbs Up!

par Danilo R d V

Feb 06, 2019

Fantastic course!

par Mo R

Feb 06, 2019

Extremely helpful course, it covers whole concepts and building blocks of CNNs, lot of thanks for coursera and Sir Andrew for giving us the chance of learning.

par Akash G

Feb 06, 2019

By Hard Way We Learn CNN..

Awesome Project And Experience..

I want Intern

par Prasenjit P

Feb 06, 2019

Great!!

par Yuri V H V

Feb 06, 2019

graciass

par Vishnusai Y

Feb 07, 2019

Excellent Course

par Arpit J

Feb 06, 2019

Loved the part where research papers were discussed. Please make more such courses with focus on past and prospective future research.

par Hassan E

Feb 06, 2019

Greeeeeeeeeeeeeeeeeeeeeeeat

par Satyam D

Feb 07, 2019

Excellent course on CNNs from Prof. Andrew Ng and his team at deeplearning.ai. Thanks a lot for the exposure on various applications of CNNs that can really make a big difference !!!

par Dusan S

Feb 07, 2019

This is an amazing course!

par Neeraj T

Jan 25, 2019

This course really made me feel like working on an actual project rather than just going through course material and solving some cooked up problems

par Andrei K

Jan 25, 2019

I improved my knowledge in CNN, so I learned new CNN architectures. Thank you!

par Dennis Y

Jan 24, 2019

Learn a lot from this course thanks NG

par 朱荣鑫

Jan 25, 2019

Wonderful!

par Zhiliang W

Jan 25, 2019

Learned a lot through this course!

par Haris M

Jan 25, 2019

Pure Gold!

par Bruno G C

Jan 24, 2019

This course is awesome! I have learned a lot and finally nailed the Convolution operation!

par Яков

Jan 26, 2019

Nice course

par Zifei S

Feb 20, 2019

Very clear lectures and hands-on experience to gain lots of experience with CV problems and cutting-edge models. I'm an NLP engineer and this course gives a great intro to DL for CV. IMHO it's one of the greatest course in the series.

par Claudio C

Feb 20, 2019

Loved it! It is only shameful how most of the model details are hidden by premade functions it would be nice to train the models from scratch

par Mukund A

Feb 19, 2019

It was a really good experience. Best course available online. Well structured and well guided assignment. Got to learn a lot. Thank You!

par Manpreet M

Feb 19, 2019

Splendid course with extremely useful content and exercises. After this course you will definitely be comfortable with CNNs.