Jan 13, 2019
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.
Sep 02, 2019
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.
교육 기관: Selina N•
Mar 20, 2020
It's an exciting course. I find very interesting to learn object detection, facial expression and face recognition. The concept of neural style transfer is easy to understand and funny to generate image to absorb the style from another image. The explanation is useful. One improvement is some assignments only import the trained models with extra source code. It would be better for students to build by themselves to go through the whole model development step by step.
교육 기관: Rahul K•
Mar 07, 2018
Very intricately explained course! Prof. Andrew has gone the extra mile here, making sure that the basics of CNNs have been imbibed thoroughly. Kudos to the programming assignments - They're undoubtedly the toughest of all the former deeplearning.ai courses. Use the discussion forums to help get subtle hints. I now feel that I can read CNN-related papers and even work on CNN applications. Plus, you learn how to implement Neural Style Transfer (DeepDream) here!
교육 기관: Chan-Se-Yeun•
May 01, 2018
CNN is a tough topic to fully demonstrate. From my perspective, the lecturer simply offer an intuitive introduction and pick up some notable variant like ResNet, and illustrate the main ideas through delicately chosen case studies. That's somewhat "clever", I think. Maybe that's not appropriate, but I mean that it's friendly to a fresh learner but far from detailed and enlightening for an advanced learner. Anyway, I get to dive deeper into this field myself.
교육 기관: Ocean•
Mar 31, 2018
As in every class taught by him, Professor Andrew Ng makes Deep Learning concepts and applications accessible. His clear explanations during the videos lead from learning the foundations to implementing modern-architecture Convolutional Neural Networks. He provides additional information about whether certain techniques are currently utilized in research and production which bring an important relevancy to the material. Thank you for offering this course.
교육 기관: Oleh.Davydiuk•
Dec 19, 2017
Great course! Gives a great boost in understanding of deep learning usage while solving computer vision tasks. Different ConvNet architectures, their application, state of the art algorithms are explained in detail. Sometimes there were issues while solving programming assingments, specially at the last week, but I truly appreciate deeplearning.ai work that gives everyone the ability to learn about this things very effectively. So 5 for this course.
교육 기관: TANVEER M•
Jul 03, 2019
The course gives the basic understanding of convolutional neural network in a lucid manner.Every concept is very nicely explained. I was having some confusion with yolo algorithm which got cleared.Also Neural Style transfer and Face verification using Siamese network were the two which I haven't heard before were very interesting. The assignments are awesome where how yolo and neural style transfer works made my concepts clear to a lot of extent.
교육 기관: Anshul M•
May 19, 2020
Great introduction into some of the recent and cutting edge work in the field of computer vision. The course's mathematical focus is good to understand the mechanics behind the use cases at the same time I liked the intuition about the steps in the process were shared from time to time for better context. Would have loved to get hands dirty on training models or tuning hyper-parameters - but understand it would need additional resources GPU etc.
교육 기관: Amit B•
Mar 19, 2020
Excellent Course. It has given me an immense insight into CNN and its practical applications. I have become that much more knowledgeable thanks to this course and its contents. Sincerely appreciate the concerted efforts of the team to lucidly explain the nuances of various concepts and at the same time provide ample opportunities to the trainees on hone their skills on practical aspects of implementing the algorithms. Kudos of all stake-holders.
교육 기관: Matthew J C•
Mar 28, 2018
Another fantastic course from Dr. Ng. In addition to object classification/recognition (which class does the object belong to?) this course should get you started with object detection (where in the picture is/are this object/s?). This course does not cover single or multiple instance semantic segmentation. Take this course (much of the coding is from scratch) & then go look at examples from your favorite API (Keras, TensorFlow, PyTorch, etc).
교육 기관: Hermes R S A•
Apr 18, 2018
There is a dedication, from the professor and the team, to teach you the most recent developments, without skipping important introductory level concepts. Having a grasp on the Imagenet winning architectures was really rewarding. The only down side was the YOLO algorithm assignment, because the notebook was a little confusing and disorganized, but you ca get the key ideas from it. All in all, it was my favorite course on this specialization.
교육 기관: JOSHY J•
Nov 06, 2019
This is the best course for those who are serious about Deep Learning and computer vision. Some of the features of the course are Well Arranged, Simple, give a deep understanding of the mechanism, etc. We will learn Image processing, Image detection, Object detection, Face recognition and face detection through this course. Weekly assignments in the course give hand-o experience with the popular deep learning frameworks and neural networks.
교육 기관: Shuai X•
Dec 18, 2017
Prior courses are almost all covered in the Stanford Machine Learning Course, which is free. If you don't want to waste time going through what the Stanford Machine Learning Course can offer, then this is the point to start to subscribe. Though it estimates 4 weeks of learning is needed, you can probably finish this course in a week. Assignments on CovNets and ResNets written in Tensorflow and Keras are mostly very good and very useful.
교육 기관: Ashutosh P•
Jun 19, 2018
This is a really comprehensive course by professor Andrew Ng. He dove down to even the smallest details, you'll realize this when you listen to the lectures carefully. Make notes of each lecture as it's a long course and there are lots of terminologies in which you could easily lose yourself, stranded somewhere in between lectures having no clue what he's talking about. All-in-all, it's easily one of the best courses I've done on CNNs.
교육 기관: Azer D•
Jun 28, 2018
Course was so helpful to understand concepts of conv nets. Also i like that Prof. Ng prepared the course with related successful papers of conv net world.One thing that i'm not happy is Coursera's Jupyter Notebook hub which I usually have problem with user authentication. Because of that I saved notebooks to my local machine, worked locally, and after completing it pasted my answers to notebook. I hope problems will be fixed soon.
교육 기관: JP L•
Nov 22, 2017
Extremely well done. Great balance between hand holding/help from the forums and effort in learning. I certainly appreciate the fact that after the course, you are ready to run in the real world working on AI endeavors. They also use all the most recent and up-to-date tools en development environments like Python notebooks, Keras and Tensorflow which makes you immediately proficient working in AI projects. Kudos to the team !
교육 기관: Souvik S B•
Nov 20, 2017
This is an excellent course and so far gives best understanding of convoluitonal Network and how it works. But the grading issues needs to be resolved. One thing I specially like about andrew NG courses is how it explains the basics and how algorithms are written from scratch for better understanding. Would be good if we could do the same for YOLO and Facenet.However the assignments are well designed for good understanding.
교육 기관: michael z•
Sep 19, 2019
Probably the best course in the specialization and the best course online on ConvNets!
Very engaging and interesting assignments, which cover advanced topics in an approachable manner. teaches current technologies (Keras, TensorFlow). The course goes into some of the math but doesn't get bogged down in it. The course includes recent developments in ConvNets such as the YOLO algorithm, Neural style transfer, and FaceNet.
교육 기관: Vipul S•
Apr 09, 2018
There are lot of things are happening in computer vision field and this course helped me in understanding the concept like convolution and their use in computer vision field. Practical advice like using existing open-source implementation or existing network architecture are really helpful.
Overall this course equipped me to understand the CNN and it's practical application in computer vision field.
교육 기관: Praphul S•
Nov 26, 2019
Some exercises very interesting, especially the last week. Why transpose was required made me reflect on the first course's content that dimensions matching will be a very useful technique to debug. Some highlights were the need for the convolution and how it reduces the complexity. The pace of the videos was good and details were very well explained (along with references which encourages to explore more on interest).
교육 기관: Tao Z•
May 31, 2019
Andrew and his teaching assistants made difficult course easy to understand. This is not trivial at all. The exams not only tested students' knowledge but also provide hands on experience on real models, which should be very handy when students want to implement their own AI solutions by themselves later on. Andrew is certainly an excellent teacher and an outstanding AI ambassador, besides being a pioneer in the field!
교육 기관: Kévin S•
Jul 31, 2018
You will go deep into image recognition and image processing related to deep learning. As this course show how to use pre-trained model, I should expect to get a model-hub (like docker-hub) like somewhere... but no.
Also I'm not sure to be able to do the exercice outside the notebook, because there is a lot of 'import' and libs to make work. An 'annexe'/'optional' course on how to setup environnement could be nice.
교육 기관: Yuwen W•
Apr 01, 2020
De-mystified sophisticated topics as always. Thru this course, I get a good understanding of the concept and basic building blocks of CNN, and the idea behind object localization, face recognition, neural style transfer.
After this course, I feel there is still a big gap between understanding the concepts and using them in the real world. Will move on to the tensorflow specialization to get more hands-on practice.
교육 기관: Mohd Z C A•
Jan 18, 2020
The lectures, quizzes and assignments are designed to help you to understand the topics, not to penalize you. Real-life applications really help me to understand the concepts and the underlying principles. Only one minor issue that I think needs to be addressed - the use of older version of TensorFlow. The latest TensorFlow is not backward compatible and causes major issue when I tried to run the codes locally.
교육 기관: ANTHONY R•
Nov 12, 2019
Excellent course with sufficient detail to become instantaneously productive, but at same time more deeper appreciation of internals that must be mastered when beginning designs don't work. Good launch point for learning new DNNs that are part of open source. Much better than Tensor Flow courses that just want you to know how to use the tool. I am ready to tackle my application which is wireless communications.
교육 기관: Leigh L•
Dec 14, 2018
This course is a wonderful journey for me. I can certainly apply CNN skills into some of very interesting fields. I have already begun to experience other styles to argument my son's photo. It is a great fun. The facial recognition technique is great to learn. I'm living in China now. Chinese government applies the FR into many public CCTV. It is interesting to observe how they are using it (to say the least :)