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.
1 сент. 2019 г.
This is very intensive and wonderful course on CNN. No other course in the MOOC world can be compared to this course's capability of simplifying complex concepts and visualizing them to get intuition.
автор: Esteban C•
8 окт. 2019 г.
Very good in-depth coverage of conv NN.
Just one little thing, week 4 Notebook assignments:
In style transfer code is not well explained how the train is actually working. In this case the input is set as a Variable instead of a Placeholder and this aspect is not mentioned or explained
In face recognition I still don't know how triple loss function is used during training
автор: WALEED E•
2 мар. 2019 г.
This course was the best I have ever taken. It gave me a big boost to carry my PhD research in robot vision with confidence of understanding what is happening all over the network and comprehension of one of the pioneer papers published in discussed in classes. Coding directly after finishing each week was the best to go to practice and apply all this knowledge gained.
автор: Ayush K•
28 апр. 2020 г.
Quite lucid and good introduction to CNN for beginners to intermediate level. I specially liked the links and discussions about different papers along the course that Andrew recommends to read. For some who has just hear about CNN, but knows about basic NN, this is a really good course to learn main things super fast and then proceed into their own personal topics.
автор: Kseniia P•
30 июня 2019 г.
Amazing course with clear explanations of how CNN works. Andrew gives you intuition and understanding of convolutions, pulling, padding, and explains the foundations in great detail, so you can understand state-of-art approaches and are ready to get hands on it. Thanks to the assignments' structure, you don't ever have to waste time on debugging irrelevant issues.
автор: Teye B•
6 апр. 2018 г.
I love this course. I only wish there was an opportunity to go step by step from looking at images, creating the dataset from the images, creating labels, applying a model, and then testing. This would help to answer a few questions that I have. However, when I read the papers recommended, I assume many of those questions will be answered, such as : why max pool?
автор: Umendra C•
10 янв. 2018 г.
Best course on deep learning for computer vision! Convolutional networks can be tricky to understand, but Andrew has presented the material in a very easy to understand format. He starts with simple ideas and concepts and then build on them in an intuitive manner. Highly recommended course for anyone who wants to understand the deep convolutional neural networks.
автор: Mehmet Ö•
4 сент. 2022 г.
The course is designed such that you are having fun while learning. Notebook assignments are helpful for making sure that you are not just watching but properly digesting the information given, by pushing you to think about logic and math behind the algorithms. People with calculus and linear algebra background will have an easier time maximizing their outcomes.
автор: Michal M•
10 февр. 2018 г.
Excellent course. Time well spent.
Simple explanations of difficult concepts.
I was able to download yolo v2 in pytorch, reconfigure it to use CPU on my Mac, and get it running on my webcam in 1h after completing Week3 assignment.
Told all my friends how awesome the course is.
Keep up the fantastic work.
Super stoked for part 5!!! and learning GANs and RI afterwards.
автор: Akshay M P•
27 сент. 2020 г.
The best course on Convolutional neural network I ever had! This course packs in a lot of information delivered in a very effective way. A glimpse into the development of various CNNs gradually builds up into state-of the-art implementations of very deep CNNs. The coding exercises gives the right amount of exposure to the frameworks and tools used in the field.
автор: Peter D•
26 нояб. 2017 г.
Great course from Andrew Ng, as always. The videos are superb in explaining some of the more recent algorithms and trends. And they provide good intuition on how to use them in your own work.
The only (minor) remark is that the exercises might not be that challenging for those that already have done some ML programming in the past.
But overall still 5 stars!!!
15 апр. 2019 г.
I was always curious about the "CNN" concept every time it emerged in the news. Thanks to Prof. Andrew's mild explanation, now I get a straight intuition into it!
The assignments were very amusing in this section. It was not hard to get a pass with the help of forums, but understanding every step is more important I think. So I will come back to practice more.
автор: MONIL J•
13 июля 2020 г.
This is the best course for beginners as well as intermediates, to learn from basics and scratch up to the advanced of CNN. In this course, the fundamentals as well as all different CNN architecture and Face validation, recognition and neural style transfer has been covered and explained in very easy language.
Thank you Andrew Ng for such an amazing Course!!!
автор: Sherif M•
19 апр. 2019 г.
Again a great course by Andrew Ng and his great team. Convolutional neural networks are the reason for the recent Deep Learning revolution or let's say better renaissance. Andrew does a great job in explaining the theory, math and application fields of CNNs while also telling about the history of recent advances in CNN algorithms and architectures.
автор: Jaime M M•
15 июня 2019 г.
As in previous courses, Andrew made understandable complex and abstract content. This course is by far more challenging than the 3 previous ones. Maybe not at the assignments as we make use of facilitating frameworks and helper functions, but to really follow what is happening behind... its another level compared to previous courses on the specialization.
автор: Ammar A•
8 окт. 2020 г.
It's thoro and concise... the best part is the assignments are interesting and we learn quite a few things in the course which talk to newbie perception of DL... so things like Face Verification yep now is the right time not only to learn how to implement but also learn the quirks & features of CNN's... Course4 all those efforts are indeed paying off...
автор: Adrien S•
28 дек. 2017 г.
Great overall course, keep teaching please ! I learnt a lot. I have a Ms degree in Machine Learning but we didnt had the time to really learn about Deep Learning. I feel it was a great introduction to the field and I feel confortable now to get more in details about everything and read papers etc.
So thanks for that, and I can't wait for part 5 about RNN
автор: P M K•
8 дек. 2017 г.
This was a really good course to see mini projects getting executed. It gave quite a lot of practical insights working on the problems. The only issue was that week 4 assignments had some bugs in code comments due to which people spend quite a lot of time debugging causing unwanted waste of tine and frustration. Please correct the errors.
автор: yuji w•
16 нояб. 2017 г.
nice program to learn about convolutional neural works. I always fascinated about convolutional networks and this course gives me the very nice introduction and sort of in-depth knowledge and first hand programming knowledge in this area. The instruction and nice and start from easy and slowly get you into the deep knowledge. Great course and nice work.
автор: Usha S•
27 авг. 2022 г.
The course is very well organised. Thanks to the entire team for their best effort. Special thanks to our 'Andrew ng' for his wonderful teaching. He has become my role model teacher. He made the course very easy to follow by his extraordinary teaching skill. Kudoos to you and your team!!
Thanks a lot. Keep looking for such courses in future.
автор: Daniel C•
31 янв. 2018 г.
This course covers the basics of convolutional neural networks. After you understand the materials covered in this course, you'll know how smart phone cameras auto focus on faces. You'll also learn the basic building blocks that powers self-driving technology. These are just two of the many cool concepts you'll learn in this course. Highly recommended!
автор: Vishaal K M•
8 июля 2020 г.
The programming exercises require much more attention than you think it does. Although it's required to only fill in the code in specific areas and not too much either, the foreword before each code section must be studied carefully if you are to build your own convnet. The video lectures are pretty straight forward, so there's nothing to worry about.
автор: Martín C•
7 июня 2020 г.
Unos de los cursos más didácticos que he realizado. Muy claras las explicaciones de Andrew Ng sobre todo con respecto a las capas que componen una ConvNet. ¡Lo disfruté! Recomendado.
One of the most didactic courses I have ever taken. Andrew Ng's explanations are very clear, especially regarding the layers that make up a ConvNet. Enjoy it! Recommended.
автор: Cem O•
10 апр. 2018 г.
Just like the other courses in this series, this course was prepared with great care to optimize the learning outcome. Clear and motivating lectures, great selection of up-to-date methods and very illustrative examples. I would like to thank Prof. Andrew Ng and all the course staff most sincerely for designing and making available these great courses.
автор: Guangyu L•
23 февр. 2020 г.
Very good learning experience. Prof. Ng gave a lot of insights about not only the CNN frameworks but also some real world working experience and hints which were very informative. For this one , I had very heavy work load during learning, I recommend people take it in a continuous manner, this helps you understand and connect every knowledge nodes.
автор: Abhishek A•
9 авг. 2019 г.
Excellent Course!! By doing the this course I am now feeling very confident in CNN. This course is very important for all whether they may or may not work in CNN/images. This fundamental learnt here can be used in other domains of deep learning.
Thank you deeplearning.ai Team for proving this wonderful course. It has opened new opportunities for me.