Chevron Left
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

Фильтр по:

276–300 из 5,334 отзывов о курсе Convolutional Neural Networks

автор: Carlos A L P

4 янв. 2021 г.

Nice exploration of CNN theory covering theory and Python exercises through different algorithms. One recommendation would be update broken links and re-write comments in code as sometimes it is not clear what variable or what is needed to complete the required functionality, specially on ungraded exercises

автор: MBOUOPDA M F

16 июля 2020 г.

This course explains the details of CNNs with a great simplicity. It also presents some state of the art CNN architectures with their ideas very clearly. Finally the assignments allow to implement several CNNs and also show how transfer learning is used to perform face recognition and neural style transfer.

автор: Alexandre M

29 нояб. 2019 г.

One of the most important courses in the Deep Learning Specialization in my opinion. Good content, enjoyed the homework, lots of details for beginners and extra resources for more advance content. Would definitely recommend for anyone interested in working in Machine Learning especially in Computer Vision.

автор: Avineil J

4 дек. 2017 г.

Exceptional Course. Learnt a lot from it. Takes a different approach to teaching than other courses in the sense that more focus is on applications rather than training of models for which a GPU cluster is a must. Thanks Andrew Ng and his team for the wonderful course. Looking forward to sequence models :)

автор: Samit H

2 авг. 2020 г.

This is the course I enjoyed the most among the Deep Learning Specialization Course threads. Seems very practical to me and I learned a lot about CNN. A few more detailed practice in notebook problems could've made things more interesting. Many thanks to Andrew Ng for making such wonderful lecture videos.

автор: OMAL P B

10 апр. 2020 г.

An amazing course to get an advance knowlege and practise "Convolutional Neural Networks". Andrew Sir makes the math and concepts behind the scenes very easy to understand. The course is easy to follow as it gradually moves from the basics to more advanced topics, building gradually.

Highly recommended.

автор: Jizhou Y

7 мар. 2019 г.

Professor Andrew is really knowledgeable. The lecture videos he makes are really helpful for me. I really learn a lot from them. Also, the recommended learning materials such as academic paper he recommend are really useful for me if I want to further my learning on the residual network or YOLO algorithm.

автор: Quentin M

1 авг. 2021 г.

Fascinating course, as usual Prof. Ng gives fantastic explanations and breaks it all down into easy to understand fragments. His style is really engaging and he is so encouraging. There are some amazing applications in the programming examples that you'll want to play with long after the course is over.

автор: J.-F. R

18 февр. 2020 г.

Great course by Prof Ng. I had taken his Machine Learning course a few years ago, so expected high standards of content and assignment preparation - I was not disappointed. Staff is very responsive and helpful in forums as well. I highly recommend it. Taken as part of the DeepLearning specialization.

автор: George Z

29 авг. 2019 г.

Exceptional course taking you into the real world of deep learning by exploring new concepts and classical architectures like LeNet-5, AlexNet, VGG-16, ResNet, R-CNN, YOLO, FaceNet and Style Transfer that propelled deep learning in new heights. Loved every part of it (minus some hiccups with the grader).

автор: Mukesh K

29 авг. 2019 г.

The course is just awesome both in terms of content that is being taught in the lectures and the assignments. Though, I think the last week is not that much important for the industry purpose but definitely it is good for those who are interested in non-industrial use of tensor flow and neural networks.

автор: Yong B S

26 июля 2020 г.

it's a wonderful course to learn CNN. thanks to the Prof. Ng for his excellent teaching. the programming assignment is clearly explained and structured. it is easy for student to follow and understand what they are doing. I am really enjoyed the learning. again, thank you very much Prof. Andrew Ng !!!

автор: Ignacio H M

26 мар. 2020 г.

I finally understand YOLO! This course has the best material available on CNNs. Even though I come from a MSc in Computer Vision and Machine Learning, we didn't have enough time to fully cover 'complex' architectures such as YOLO. Thanks to this course I feel more up to date in the Deep Learning field.

автор: Victor F d P

8 апр. 2020 г.

Once more Andrew steps up as a brilliant teacher. I'm a biologist looking to improve my data science skills to better tackle medical imaging problems. I'm confident to say Andrew is the reason I'm going to make a difference in low resource communities in the future. Thank you, Andrew, you are awesome.

автор: Scott H

5 февр. 2018 г.

I really enjoyed this course. I found it pretty approachable. FWIW, I'd taken Andrew's original ML class, but then skipped 1,2, and 3 of the new one (and jumped into 4) The course really holds your hand, so be prepared to force yourself to try some of this on your own to be sure you've understood it.

автор: Harsh B

6 нояб. 2017 г.

This course is intended for ML learners who have background knowledge of NNs and want to enhance their scope of knowledge in CNNs. Prof. Andrew has been an amazing instructor. The material used in this course is mostly based on Tensorflow, so make sure to have a bit of prior knowledge in Tensorflow.

автор: Kevin C

28 окт. 2020 г.

El mejor curso hasta ahora (me falta el de RNN). Los temas son bastante interesantes y sus aplicaciones hacen que el curso sea muy bueno, tanto en los cuestionarios como en los ejercicios de programación. Quizás sea necesario el feedback en los cuestionarios para saber por qué algo está bien o mal.

автор: พสิษฐ์ จ

9 апр. 2020 г.

I have learnt a lot new things in this course, constructing exciting image/object detection projects with Tensorflow, Keras and even plain Numpy. Also, Andrew well explains many complex network architectures which illustrate various perspectives of the applications of convolutional neural networks.

автор: Vidar I

13 февр. 2018 г.

This course really gets you started working with CNN. The only downside are the "bugs" in the assignments. My advise is to read the discussion forums before you do the assignment to know if there is a bug that you should know of before submitting.

Beside this minor bug, the course content is 5 star.

автор: Pranab S

17 окт. 2020 г.

I am loving the journey I have started with DeepLearning.AI .Thank you to the amazing Teacher Andrew Ng. Sir for offering such wonderful online course which is affordable to anyone anywhere in this world. Thank you so much and I am looking forward to finish the last course of this specialization.

автор: Aravind R K

20 июня 2021 г.

What an amazing course! The content delivered in this is superb and up to date. Deep Learning.AI never fail to impress me. The lectures were super cool with the content being crisp and well delivered. The assignments were also super fun to work on and very interesting. Overall, a great course !!

автор: Chitrao S R

13 июля 2020 г.

I liked everything about this course ! The instructor was very good at explaining the complex concepts and I really liked the quizzes and programming assignments , they really help brush up the concepts taught in the respective week !!! I would like to thank Andrew Ng for this amazing course !!!

автор: Damian C

26 мар. 2018 г.

Really enjoyed learning more about the current state of the art of image recognition models. Although the structure needed can be at times overwhelming, the concepts are clear and implementation via open source packages make it feasible. Many thanks for making this available, keep the good work!

автор: Maciej F

8 мая 2019 г.

Somehow, a bit harder than rest of the courses for me. I had problems with tracking dimensionality and tensorflow notebooks were hard and difficult to debug. I think it would be nice if tensorflow has its own as a course or 2 weeks maybe. But anyway the concepts explanations is great as always!

автор: Juan M E B

18 апр. 2018 г.

Excelent course. ConvNets are an eye-opening subject and the course explains the main concepts and applications in a simple way, indicating the source papers to understand better. I'd only ask for a couple of videos explaining in more detail backpropagation and the upload of the missing slides.