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Отзывы учащихся о курсе Convolutional Neural Networks от партнера

Оценки: 40,154
Рецензии: 5,316

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

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

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.

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326–350 из 5,289 отзывов о курсе Convolutional Neural Networks

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 for this opportunity to get closer to finishing my new carrier journey.

автор: Janzaib M

6 мая 2018 г.

Very very well designed homework. Gave me a really close feel of deep learning for computer vision. The great thing is, in this course you play with very very state of the ConvNet architechture. Thank you so much Professor Andrew NG and your team. A very big contribution you have done.

автор: Huang C H

24 нояб. 2017 г.

Convolutional Neural Network are exciting to learn, but its concept can be quite abstract. However the materials are delivered progressively, and in a concise manner. The programming exercises are challenging. I hope there was more in-depth introduction to Tensorflow and Keras, though.

автор: Evandro R

15 дек. 2020 г.

Another great course by DeepLearningAI and professor Andrew Ng. Convolutional Neural Networks are an amazing part this great field of Deep Leaning that is Computer Vision. Professor Andrew Ng it's simple amazing at teaching those concepts, it almost feels like magic! Wonderful course!

автор: AKSHAY K C

19 мар. 2020 г.

The course had a very clear outline starting from the basic fundamentals of CNN and progressing steadily towards the applications ranging from facial recognition to neural style transfer in the final week. Kudos to the instructor and his team for delivering such an outstanding course.

автор: Feng W

15 мар. 2019 г.

I have some problem doing week four programming assignment "Happy House Face Verification/Recognition". The pre-trained model "FRmodel" wouldn't be loaded (waiting for over half hour). I still managed to submit the assignment and passed the test without running out the correct result.

автор: Malek B

24 дек. 2017 г.

it is my second courses in coursera after Machine learning by Andrew Ng and Stanford university, I'm very satisfied by the courses quality and encourage you to go further, I'm a follower of coursera courses and one day I will contribute to share more knowledge using coursera platform.

автор: Sami

15 февр. 2018 г.

i think that's the most important course for me, of course all of them, where very very useful, but being an undergraduate Robotics engineer, the most essential thing is to learn image processing and how to make your robot think and learn and detect object and learn from environment.

автор: Wooshik K

11 февр. 2020 г.

Thank you for the lecture contents and programming problems. I am quite sure that I have acquired much knowledge and it will be very helpful to solve my own problems. Also, it would be much more helpful if there are some comments on how to build filter coefficients or filter banks.

автор: Sathiraju E

5 авг. 2019 г.

Amazing course. A lot of knowledge packaged into one package. This has been the most useful course in the Thank you Andrew and team. Lot's of interesting stuff and knowledge has been shared out here. Only the back propagation for CNN was missing but otherwise great.

автор: Yernur N

18 июля 2019 г.

It is an essential course for those who wants to boost their general knowledge in the area of CNNs. It will give you a great foundation to build on your career and further learning. I struggled a bit with Keras, but I am planning on taking another course to learn this field further.

автор: Matheesha A

21 июня 2019 г.

This is an excellent course to learn the concepts of Convolutional Neural Nets. The hands on experience by the weekly assignments were very helpful to understand the concepts. I strongly recommend this course for the students who are interested in learning CNNs. Thanks Prof. Andrew.

автор: Ravi P B

17 апр. 2020 г.

A very detailed and pleasing insight into the amazing world of Convolutional Neural Networks and as always Andrew Sir has been absolutely brilliant in the lectures.This course presents an in depth knowledge of the challenges and various technologies in the field of computer vision.

автор: Xiaolong L

5 февр. 2020 г.

Excellent course! The programing exercises are both realistic and let you build (toy version) of state of art CV system. Many reference to heavy weight papers in the domain in the course, which student who really want to get into DL and CV can read and further expand their horizon.

автор: MADAN M

21 февр. 2018 г.

I got thrilled by the lectures and its assignments. One thing that I would request is a lecture on how to use pre-computed models, in all the assignments we are using pre-computed models. Andrew explains why we should use them but in practice its seems little difficult to use them.

автор: Vijaya R S G

6 нояб. 2020 г.

This course is inspiring & really good. It presents with real research in a very lucid and simple manner.

I really liked the explanation of YOLO algorithm, was fascinated by it. With just one course to complete I am becoming fan of Andrew Ng and also other heroes of deep learning!!

автор: sushant

12 сент. 2020 г.

Very Helpful for me! Andrew combine basic knowledge on ConvNet with advance architecture and application. The course assignments, lectures are all good.

I will choose Coursera my first Choice, as it will give financial aid too. Thanks for the instructor to making such good course.

автор: Shaelander C

9 дек. 2019 г.

Very informative course . Professor Andrew Ng has done a great job of explaining most of the concepts of CNN. And Assignments are really good to apply what we learn in the lectures. Professor Andrew is the best professor I ever came across the style of his teaching is unmatchable.

автор: Guoliang

17 апр. 2020 г.

This is a very detailed introduction to ConvNet with descriptions of some modern ConvNet architect. Though I feel that if the programming assignment could be much better if we can implement some of these algorithms from scratch with efficient implementation (using Google Colab?).

автор: Siraprapa W

10 окт. 2021 г.

Prior to this course, I have been thru so many other elearning about the exact same topics, but none have them gave such a crystal ,clear, and easy to understand explanation. It is very enjoyable learning journey. Thank you, Andrew and the content team for such a wonderful jobs!

автор: Hasaan A

30 июля 2020 г.

Learned some really exciting stuff. It was great to learn a lot of the classical networks like resnet etc. Although, I wish the programming exercises did not have most of the stuff already filled in (though I understand it is done to make it easy for beginners to complete them).

автор: Dave J

6 апр. 2020 г.

The material is clearly explained by Andrew Ng in his calm yet enthusiastic style. Programming exercises are well structured and explained: if anything I find there's too much hand-holding but having got the basics, there's nothing to stop you experimenting further on your own.

автор: Animesh S

21 мая 2019 г.

Great course, concisely conveys both techniques and advice for practical implementation of Neural Networks in Image recognition. Great for a person who is already familiar with the idea of Deep Learning and want to take it forward, and ties in perfectly with the specialisation.

автор: Ali S

10 авг. 2018 г.

This course is a perfect way to teach these high-level concepts. They made it easy, step by step, and practical. You can learn not only convolutional neural networks in both conceptual and practical way, but also a lot of tips and tricks about tensorflow, Keras and even python.