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AI Capstone Project with Deep Learning (으)로 돌아가기

IBM의 AI Capstone Project with Deep Learning 학습자 리뷰 및 피드백

210개의 평가
35개의 리뷰

강좌 소개

In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate it. Learners will then present a project report to demonstrate the validity of their model and their proficiency in the field of Deep Learning. Learning Outcomes: • determine what kind of deep learning method to use in which situation • know how to build a deep learning model to solve a real problem • master the process of creating a deep learning pipeline • apply knowledge of deep learning to improve models using real data • demonstrate ability to present and communicate outcomes of deep learning projects...

최상위 리뷰


May 23, 2020

A very nice project based course to get hands on experience with deep learning\n\nand transfer learning.


May 07, 2020

The Course is good, The labs were crashing which were causing lot of issues in completing the course

필터링 기준:

AI Capstone Project with Deep Learning 의 34개 리뷰 중 1~25

교육 기관: Jeremiah J

Feb 24, 2020

I guess the course was OK, but the complete limitations of the "provided" compiling environment is inexcusable. I tried five different emulators, eventually using Google's Colab tool in order to get any type of results within six hours. I don't know what you can do, but something needs to be done.

Also, I would recommend NOT having two different tracks (Keras & Pytorch). Because of the aforementioned coding issues, most of the real instruction occurs in the discussion forum. It is INCREDIBLY confusing when there are essentially two different assignments posting questions in the same space. Also, can you do something about all my "classmates" asking for people to review their work in the forum? In my opinion, that is NOT the purpose of the discussion forum. As the admins, can you please just delete those requests to make it easier to find the REAL discussions. You are smart computer scientists, can't you create an AI to filter all those posts into the bit-bucket?

교육 기관: Hernán C

Apr 02, 2020

Its takes to long to train the models (6 hours each case). I was lucky because some students give me de advice to use google Free GPU to complete the each train in 2 minutes. Without the students tips It is impossible.

I suggest to add to this course information about to use either cuda with own local jupyter lab or either recommend some serice like googel colab to get better performance.

교육 기관: Eric

Jan 29, 2020

Course opened late. One instructor did not prepare his materials and the exercises were not even accessible. I will not be purchasing from Coursera in the future because of this specialization course. Truly a huge waste of money.

교육 기관: Lam C V D

Feb 20, 2020

But need to study extra as these topics are not taught like Transfer Learning

교육 기관: Bhaskar N S

Apr 04, 2020

Most of this course is lab-work. However, the lab environment was inadequate. It kept crashing, disconnecting, or went to slow. While I understand that the Lab is a 3rd party tool, my payment was made to Coursera, hence they need to help ... at least by extending access for the lost time.

교육 기관: Ravi P B

May 23, 2020

A very nice project based course to get hands on experience with deep learning

and transfer learning.

교육 기관: Sarath C G K

May 07, 2020

The Course is good, The labs were crashing which were causing lot of issues in completing the course

교육 기관: A A A

Jul 08, 2020

I got a chance to put what I learnt into practice and the idea of choosing between Keras Track or PyTorch Track was very beautiful. I can suggest another track for TensorFlow, making it a choice between choosing from 3 tracks instead. That would feel more complete.

교육 기관: Richard B

Jun 04, 2020

Putting in practice what I learned and experienced positive results was very satisfactory.

교육 기관: Branly F L

May 15, 2020

Excellent work from the teachers, thanks for your efforts.

교육 기관: Sanchit K

Apr 04, 2020

Please labs are not so good. Please improve it.

교육 기관: Anas O

Jun 13, 2020

Thanks Dr. Alex, I always love your courses

교육 기관: Amine M B

May 09, 2020

Very interesting and helpful

교육 기관: Suprakash S

May 11, 2020

Excellent course!

교육 기관: Krish g

Jun 04, 2020

Marvelous course

교육 기관: Julien V

Jun 03, 2020

Great course !

교육 기관: Christos

Feb 25, 2020


교육 기관: Carlos F C d S e S

Mar 26, 2020

Thank you!

교육 기관: Alvaro A B A

Apr 06, 2020


교육 기관: Claudia S

May 17, 2020

For the Keras part, it would be desirable if "clean" zip files were provided for week 2 to week 4 exercises, since they contain the MacOSX folder (which I think it is not required for the exercises). Also for Keras, it might be helpful if any other example could be found, since I do not think that using models which take that many hours (35 hours in Cognitive AI site / 8 hours in Google Colab) contribute in any way to the learning process. Or at least adjust them to use one epoch, like the Pytorch exercises

교육 기관: Yash R

May 28, 2020

This is an excellent course if someone wants to learn transfer learning. However, having said that, there should be another task for which students should build their own model and compare its accuracy with the predefined one. With this, students would get insights as to how to build a deep learning model from scratch.

교육 기관: Julien P

Jun 19, 2020

It's a great course to guide you through the full process of training a deep neural net. However, one needs to use external resources to train the model efficiently (Google Colab for example). The resources provided by IBM are not powerful enough to train the model in a reasonable amount of time (no GPU).

교육 기관: Daniel J B O

May 27, 2020

I like the flexibility to pick our framework for the project i wish the kers one were a little bit more challenging

교육 기관: Thar H S

Mar 27, 2020

Thank a lot for creating this course. It really useful and practical for me.

교육 기관: charles l

Feb 24, 2020

This course was riddled with operational flaws regarding the image data, and how it operated in the IBM framework. At one point I was not able to run the labs with either PyTorch or Keras versions, and eventually just downloaded the notebooks and ran them in Google Colab to complete the specialization.