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Convolutional Neural Networks(으)로 돌아가기

deeplearning.ai의 Convolutional Neural Networks 학습자 리뷰 및 피드백

4.9
28,058개의 평가
3,381개의 리뷰

강좌 소개

This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: - Understand how to build a convolutional neural network, including recent variations such as residual networks. - Know how to apply convolutional networks to visual detection and recognition tasks. - Know to use neural style transfer to generate art. - Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization....

최상위 리뷰

AG

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.

RK

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.

필터링 기준:

Convolutional Neural Networks의 3,344개 리뷰 중 76~100

교육 기관: Hari K M

Jan 19, 2018

Really good course but relatively tougher than the previous ones. Learnt a lot with best part being able to learn state of the art algorithms and implementations. Did felt kind of oblivious at times while doing the programming assignments but the discussion forums came in handy during those times. There are some issues with the grading of last programming assignment which I think will be resolved soon. I definitely recommend this course to everyone who wants to specialize in neural networks.

교육 기관: Dhritiman S

Dec 09, 2017

The material in the course was very good. Andrew Ng is a fantastic instructor and is able to convey concepts in the most intuitive way.

This course would be perfect, but for the problems with the last two assignments (Face Recognition and Style Transfer). There were errors in instructions and grader solution wouldn't match solution expected in the notebook. The only way to figure out how to pass the assignments was to dig into forum posts where information was provided in a haphazard way.

교육 기관: Paulo A F

Nov 09, 2017

Great course. It has all the main state-of-the-art approaches. I just missed dealing with 3D data (RGB-D and point clouds). I believe the programming assignments get better as the course progresses because they get more demanding.

This is a great overview course. I suggest anyone interested in deep learning vision to start with this course and then move on to implement a CNN in tensor flow form scratch using one of many tutorials online.

Thank to the team for this great course!

Best regards,

교육 기관: Matei I

Mar 03, 2019

A lot of quality content in this course. The first half focuses on the intuition behind ConvNets and their implementation, while the second half focuses on applications. I thought that the neural style transfer application was particularly enjoyable. My only suggestion for improvement is to let the students do more work in the assignments for the last two weeks. I feel that most of the code in these assignments was black boxed, and I got to implement a minimal portion of the algorithms.

교육 기관: Martin B

Sep 01, 2019

As with all the other courses by Andrew Ng, pacing and presentation are perfect. Learning this material is highly rewarding. Programming assignments are clear and accessible, although a little bit more thorough introduction in the use of Keras and Tensorflow wouldn't hurt in some cases. I found myself pretty deep in the documentation of both libraries - although that might be part of the intended learning process. Highly recommended! - Thanks to professor Ng for making this available

교육 기관: 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.

교육 기관: 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 .

교육 기관: 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. :-)

교육 기관: 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.

교육 기관: HEF

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.

교육 기관: 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

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: 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.