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Вернуться к Convolutional Neural Networks

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

4.9
звезд
Оценки: 40,520
Рецензии: 5,372

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

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.

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

Фильтр по:

476–500 из 5,347 отзывов о курсе Convolutional Neural Networks

автор: Karan M

13 нояб. 2018 г.

A very wonderful course! A must for people who want to enter the field of Computer Vision using Deep Learning. Core fundamentals are taught very clearly such that after doing the course, student can venture into the field on his/her own.

автор: Lucas B

7 апр. 2018 г.

Substantive and relevant, yet clear and straightforward. My only recommendation would be to add some GitHub links and/or optional assignments in order to give slightly more open-ended assignments that require more than filling in blanks.

автор: Carlos V

19 янв. 2018 г.

Another excellent course by professor Andrew Ng and Coursera, the level of explanations and material are excellent, the detail in those Jupyter Notebooks is fantastic, I highly recommend this course to anyone interested in Deep Learning.

автор: Yun-Chen L

19 мая 2020 г.

This course had more technique skills, like CNN. maxpool. Residual network. triplet loss. YOLO model. style transfer. I like assignments because it give you some research papers and examples in the real world, that will make you better.

автор: Thota m s s

3 нояб. 2019 г.

Its the best course where you can practically implement your own learning algorithms the best thing was I implemented a famous ResNet on my computer and that great . Anyone interested in CONVNETS should definetly try this great course

автор: Vincenzo P

20 мая 2018 г.

Great course! Classes of Andrew Ng are, as usually, crystal clear about necessary theory and full of precious hints for efficient implementation of CNN. I recommend it to everyone seriously interested in Computer Vision advanced tasks.

автор: V

31 янв. 2018 г.

Hardest of the 4 so far. There's more autonomy required in programming and shape calculations require really understanding how ConvNets work. But the more difficult it is, the more worthwhile and non-trivial the achievement becomes. :)

автор: Markus L

20 нояб. 2017 г.

Excellent overview of CNNs including practical exercises with appropriate level of details. Gained good understanding what one can accomplish with CNNs and where to start. Also gives good idea of practical implementation costs of CNNs.

автор: Phan Q K N

18 февр. 2022 г.

It was such a great course. I got a chance to study and apply fundamentals of ConvNet to many practical problems such as Face Recognition, Object Detection, Semantic Segmentation, Neural Style Transfer which I have longed for knowing.

автор: Veeraraghavan N

14 июня 2020 г.

The course is really good with in-depth explanations of the concepts in a clean, clear and precise manner that is both easy to understand and implement. The programming assignments are fun to complete and test out. Highly Recommended!

автор: Raymond S M

25 июня 2019 г.

I found this to be an excellent introduction to convolutional neural networks. I was already very familiar to convolution but I could see that if I wasn't it would have been clear. All concepts were explained well and I learned a lot.

автор: David R V O

10 мар. 2019 г.

I think this course is excellent and I'm already applying the skills I've learnt from it to my current research. I would have preferred a little bit more focus on the theorical part of ConvNets, especially backprop. 100% recommended.

автор: Zifei S

20 февр. 2019 г.

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.

автор: Dao M C

3 мар. 2021 г.

Thank you very much Andrew Ng. The course helps me understand very well about Convolution Neural Network. Lectures go from easy and then combine together, even assignment, I feel very excited and happy to learn in the teacher's way.

автор: Devavrat S B

26 мая 2020 г.

This course is very good if you want to enhance your knowledge about CNNs, the course contains different CNN architectures and use cases, the way Andrew Ng Sir teaches every concept in detail along with visualization is appreciable.

автор: Yuezhe L

19 нояб. 2018 г.

This is an immensely helpful class. I have been wanting to learn imaging processing and machine learning, and this class helps me get started. Using what I learnt from this class, I was able to implement CNN to help my own research.

автор: Taras M

29 июля 2018 г.

The most interesting course in the whole deep learning specialization, a lot of practical cases and much closer to the deep learning state of the art. Kudos for face recognition and neural style transfer (yolo is super cool as well)

автор: Ahmad J

26 янв. 2021 г.

Fantastic overview of CNN's from a historical perspective but also from a practical and theoretical perspective. Basically all facets of CNN's have been very clearly discussed. Andrew Ng is just a pleasure as a teaching instructor.

автор: Li W Y

8 янв. 2018 г.

This is a great course which make me know how to do computing vision and neural style transfer (which is something I thought amazing before). Although the course is a bit difficult, it is interesting and useful. Hope you enjoy too.

автор: Stuart H

30 нояб. 2021 г.

I found this more challenging than the previous courses, but learned a lot. The concrete examples of CNNs were really helpful to picture how they are built in practice and the exercises helped me understand tensorflow much better.

автор: Azmyin M K

29 нояб. 2020 г.

Week 3 was most relevant to my work on Indoor Positioning System. I felt like week 4 could have been made optional. Other than that, excellent material, easy to understand and digestible for us students from engineering discipline

автор: Gautam D

11 янв. 2019 г.

Wonderfully explained! Andrew and team have been kind enough to provide all the important papers and documentation required too. Very well laid out course. Can't wait to finish the 5th and final course! Thanks team deeplearning.ai

автор: Bhaskar G

26 сент. 2018 г.

An optional course on Tensorflow/Keras and their comparison with other prevalent frameworks would have given a nice touch. I realized that lot of handholding is needed in assignments just because the basics if TFlow are not clear.

автор: 胡帆

27 февр. 2018 г.

Excellent course! And the programming assignment is necessary if you want to know deep learning deeply, the video is shallow and it is more like an introduction to deep learning . Anyway, Andrew Ng is absolutely a great teacher!

автор: Jack Q

31 янв. 2018 г.

This course offers quite plentiful materials. I learned lots of models whose performances are state-of-the-art. Brief and intuitive description of these models helps me a lot when reading the corresponding research papers. Thanks!