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Вернуться к Convolutional Neural Networks

Отзывы учащихся о курсе Convolutional Neural Networks от партнера deeplearning.ai

4.9
звезд
Оценки: 40,450
Рецензии: 5,359

О курсе

In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more. By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Лучшие рецензии

AG

12 янв. 2019 г.

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.

AR

11 июля 2020 г.

I really enjoyed this course, it would be awesome to see al least one training example using GPU (maybe in Google Colab since not everyone owns one) so we could train the deepest networks from scratch

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301–325 из 5,334 отзывов о курсе Convolutional Neural Networks

автор: Prakhar P

12 июля 2021 г.

This is one of the best courses to understand CNN and to have a strong grasp on the fundamentals of Computer Vision and various architectures. I am really happy to have enrolled for the Deep Learning Certificate course. I recommend this to anyone interested in diving deep into neural networks.

автор: Andrei N

21 сент. 2019 г.

The content, examples, assignments, and quizzes are thoroughly developed. All the courses of the specialization share the same notation and lead a student from basic concepts to complex ones helping to develop an intuition on each step. The best course on topic of Deep Learning one could find.

автор: benedikt h

10 мар. 2018 г.

great ! It is complex though, don't get fooled by the doable exercises - to really understand you can take several loops.

Imagine someone breaks up recent complex research paper into python notebooks for you and you get this delivered like a delicious food - this is how I feel about this class.

автор: Jun W

16 дек. 2017 г.

This is an excellent course. Although I've got 100%, there are still some details and intuitions need to be figured out. Maybe I will go over it again. And of cause, I'm looking forward for the fifth course. I wish the fifth is not the last course. We still need to know reinforcement learning.

автор: Dr. R M

7 нояб. 2017 г.

Very informative lectures with simple explanations of what the algorithms are doing. The programming assignments are extremely detailed and well explained. This makes it very efficient and fun to learn the concepts of Conv Nets, Res Nets, the YOLO algorithm and so on in a short period of time.

автор: Jonathan M

15 июня 2020 г.

A great course overall. Ties together the concepts presented in the first 3 courses and does a great job of showing some very practical real life applications - the programming assignments show a wide range of practical applications of deep learning like face recognition, art generation, etc.

автор: Raúl A d Á

17 мая 2020 г.

It was a great course. You end up with a pretty good understanding of convnets and their different applications and algorithms. For sure this course set up the basis for image processing work and research, although it is necessary to refresh concepts and go over the notebooks to fix concepts.

автор: Nour A

7 янв. 2019 г.

The course explains topics I used to consider as "complicated" in a very clear and simple way. The videos and quizzes about theoretical concepts accompanied with programming assignments and extra reading material give solid understanding of the topic, its current trends, and future direction.

автор: Igor C

4 нояб. 2018 г.

I think that should have an optional video with the mathematics behind the convolution/cross relation, showing element-wise operations on a small volume with more than one channel. I know most people will find it boring, but i think it will make easier to fully comprehend the 1x1 convolution.

автор: Wei F

17 дек. 2017 г.

Really enjoyed learning this course. I'm a PhD Student in CS but neither in computer vision or NLP. I feel like these courses are sort of jump-starter, if you would like to learn more about DL and to be expert, there's a long way to go. However, this is really a good starter!! Thanks Andrew!

автор: Sawyer S

15 июля 2020 г.

I think this course offers enough technical details for me to understand how Conv Nets works. However, I find it much easier to undertand the contents if you take the Practice in TensorFlow first, where there is a more practical focus, and understand the big picture. Overall, great course!!

автор: Adarsh K

4 февр. 2020 г.

The best place to start Computer Vision! You'll get to implement state of the art Techniques in CV, most with practical Application. The quizzes are very well designed and test your concepts. You'll learn to use open source implementations and build on top of that as well. Wonderful Course!

автор: Dipo D

11 янв. 2020 г.

Like the other courses in the DeepLearing.ai certification, this course was also very crystal clear in teaching the concepts. Now, I can confidently read additional materials on Computer Vision. The assignments were also well thought out, kudos to all the TAs. Thanks for the awesome course.

автор: Rahuldeb D

4 сент. 2018 г.

Another exceptional course offered by Coursera. There are lot of new concepts to learn in this course.

Prof. Andrew Ng has explained each and every concepts in very lucid manner. I want to give a big thanks to Andrew Ng and all other teaching associates for offering such a beautiful course.

автор: Brandon W

24 нояб. 2017 г.

Students had some technical issues throughout this course, with the autograder not correctly grading the assignments despite having all expected outputs correct. In time, I hope these issues can be fixed. However, given the level of instruction and quality of the course, still deserves a 5.

автор: Anoop P P

10 июня 2020 г.

The course has balanced of theoretical and practical aspects of Convolution neural network. Moreover, practical sessions encouraged to create a CNN from scratch, use a pre-trained model to fulfil the task. The assignments has helped to practice hands-on using tensorflow and Keras platform.

автор: Ajay S

30 авг. 2019 г.

really a great course for the image learning . i love this course well . and thanks for providing me the financial aid for the course . this will really help me to complete my research work on time .

Thnaks. for the profession Andrew Ng . for the designing and teaching a wounderful course.

автор: Soumadiptya C

15 сент. 2020 г.

As with all other courses in the specialization "Excellent". Frankly not much needs to be said about Andrew NG's lectures. The only problem I faced was in understanding the Neural Style transfer Topic but doing the programming exercise helped understand the theory behind even that Topic.

автор: Shubhang A

28 авг. 2020 г.

Amazing Course, now I have pretty good idea of image processing and convolutional networks. Fun part in this course was definitely last week where i got the basic idea of how to implement face verification and face recognition as well a good idea of Neuro style transfer learning algorithm

автор: HE Y

24 июня 2020 г.

I think this course offers an excellent illustration of convolutional neural network for beginners, even for those who have a basic knowledge about the neural network. The two applications of CNN are quite interesting and useful. I have learned a lot through this course and thanks Andrew!

автор: RUDRA P D

20 июня 2020 г.

Amzaing course on ConvNets but in my perspective anyone who wants to opt this course must have basic understanding how Tensorflow works and basic operations in it. Except every concept are well explained and also research papers are given (for who wants to dive deeper) in the assignments.

автор: Sean C

20 февр. 2018 г.

Andrew Ng's explanation of Inception Networks greatly helped to demystify more complex-looking architecture diagrams in Google's Inception Net. This course helped a lot in being to be able to understand the base building blocks, as well as their arrangement & purposes within the network.

автор: Vincenzo M

26 нояб. 2017 г.

Another super course from Andrew Ng and his team. As the other courses of the specialization, it presents the core concepts clearly. The exercise are foundamental to retain the concepts. As a suggestions, I would substitute the style transfer with an example more useful for real problems.

автор: Nobumasa

29 мая 2021 г.

This course gives me a basics of applications of deep neural networks in the field of computer vison, including face recognition, object detection, style transfer . Furthermore, Andrew provides insightful intuition for convolutional neural networks, which can be applied to other fields.

автор: Niklas T

2 авг. 2020 г.

Great course, I learned so much about ConvNets.

Thank you to Andrew Ng and his team.

I loved that they were referring to so many scientific papers. Like this you really get the chance to read them yourself and immerse yourself in up-to-date scientific research in the deep learning area.