Dec 12, 2019
Great Course Overall\n\nOne thing is that some videos are not edited properly so Andrew repeats the same thing, again and again, other than that great and simple explanation of such complicated tasks.
Jul 12, 2020
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
创建者 Camilo G•
Jan 14, 2020
Curso excelente. Da todos los detalles más importantes sobre redes convolucionales, incluyendo las matemáticas que las hacen funcionar (incluso explica backpropagation en un ejercicio opcional) y cuáles son y cómo funcionan las aplicaciones más importantes. Omite una que otra cosa, por ejemplo cómo aplicar vectorización a todos los ejemplos de entrenamiento, y de vez en cuando durante los videos secciones de audio se repiten por alguna razón, pero mayormente está bastante completo.
创建者 Mihai L•
Feb 19, 2018
This course is still amazing. Finally understood what CNN's are for and how to use them.
This is the first time in deeplearning.ai specialization that I had to consult the forums. by far implementing in low level code convolutions (first asignment) was the most difficult part.
Spent more time then with the other courses but it was time well spent. Again Andrew NG delivers a good course.
The minor editing problems in videos are the only issue that might be raised with this course .
创建者 Andrew K•
Dec 29, 2017
The entire course is great, from the lectures by Andrew Ng, to the homework assignments, and the TA's help on the forums. The really terrible part of the course is the coursera grader, which I had to hack for 3+ hours just to pass an assignment. I dont wanna dink the review for this because the class itself is wonderful. But please fix those technical issues. So the 5 stars come from averaging 10 stars from the course itself, and 0 star for coursera technical issues. :-)
创建者 Omar S M•
Sep 16, 2019
This is an excellent course in which Professor Andrew Ng explains the concepts of convolution, pooling and convolutional neural networks very well. Also the various advanced convolutional network architectures and various applications in computer vision are discussed in an excellent manner along with references to the research papers on which the content is based. The programming assignments are also excellent and really help you learn the principal concepts and techniques.
Jun 02, 2019
Before taking this course, I thought computer vision had a difficult learning curve. After taking it, I found that many difficulty materials are omitted so that I could learn without too much pressure. While I could still look into algorithm details because many papers are recommended. The programming assignments cost me a little more time than the previous courses, but bring so much more fun! I felt quite proud of myself when I successfully built the CNN in my assignments.
创建者 Ashwini J•
Jan 01, 2020
Thanks to Andrew Ng and team for putting together great content around Convolutional Neural Network. This is a fairly complex course, I needed to go beyond content provided in this course, specifically around understanding dimensions resulting from a convolution operation applied on an input image. This could be because it is hard to imagine a 4-d object. Otherwise, good content put together, assignments are good and useful starting point for projects in actual practice
创建者 Shyam C N•
May 17, 2020
This course was one of the better ones in the specialization. I enjoyed it very much. The assignments are a bit more practical, and require some thought while debugging. Although some TensorFlow experience from Course 2 is expected and useful, this course requires some additional reading of the TF and Keras manuals. My only suggestion to the development team would be that they improve the NST assignment's introduction of TF methods like assign() and InteractiveSession.
创建者 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.
创建者 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!
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.
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.
创建者 CHEW L W B•
Jun 15, 2020
Great intro to CNNs, how they work, how to use them and the types of problems they are good at solving. I'm glad Prof Andrew Ng touched on more advanced topics such as image detection, localisation and face verification/detection and how CNNs can be applied to such use-cases. The programming problems were challenging but not overwhelming, as long as one is willing to spend some time to understand the concepts presented and explained in the lectures.
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.
创建者 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.
创建者 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.
创建者 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.
创建者 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).
创建者 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.
创建者 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.
创建者 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.
创建者 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.
创建者 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.
创建者 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 !
创建者 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.
创建者 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.