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Вернуться к Convolutional Neural Networks

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

4.9
звезд
Оценки: 39,855
Рецензии: 5,269

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

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

OA
3 сент. 2020 г.

Great course. Easy to understand and with very synthetized information on the most relevant topics, even though some videos repeat information due to wrong edition, everything is still understandable.

Фильтр по:

301–325 из 5,240 отзывов о курсе Convolutional Neural Networks

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.

автор: CH L

22 мар. 2020 г.

This course teaches CNN from the very beginning to the most details. Its examples and assignments are very impressive for people to know what happen in the model and how it works for many different applications. I can realize most CNN-related research papers after finishing this course.

автор: Mohd F

23 июля 2019 г.

Convolutional Neural Networks by Andrew Ng is a Great course to start into the of CNN's Terminology for DeepLearning. This course provides me with a solid background in how the Convolutional Neural Networks works internally. Great lectures ........... Great everything thankyou Coursera

автор: Rahul S

30 апр. 2020 г.

This course gives you adequate foundation to build upon your knowledge in the subject. The structuring of course is perfect and assignments help to pick up difficult codes so easily. Andrew is an exceptional teacher who knows the field and shares his experience and knowledge so humbly.

автор: Miroslav M

24 апр. 2019 г.

I've gained very important knowledge for Image verification and recognition algorithms using ConvNet models. These models are used nowadays powering robots and self-driving cars. Thank you very much deeplearning.ai for this opportunity to get closer to finishing my new carrier journey.