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Вернуться к AI Capstone Project with Deep Learning

Отзывы учащихся о курсе AI Capstone Project with Deep Learning от партнера IBM

4.5
звезд
Оценки: 233
Рецензии: 40

О курсе

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...

Лучшие рецензии

RK

Jul 31, 2020

The capstone of the project was really good it helped me to understand the deep learning concepts clearly for providing the solution.

RB

May 23, 2020

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

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1–25 из 39 отзывов о курсе AI Capstone Project with Deep Learning

автор: 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?

автор: 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.

автор: 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.

автор: Lam C V D

Feb 20, 2020

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

автор: 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.

автор: Ravi P B

May 23, 2020

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

and transfer learning.

автор: 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

автор: 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.

автор: Mel A

Jul 19, 2020

Although my laptop's GPU wasn't up to par, I managed to run the labs and finished the project with a lot of patience . Recommended course for AI enthusiast, data analyst and role as an engineer in data science.

автор: RATHEESHWARAA K

Jul 31, 2020

The capstone of the project was really good it helped me to understand the deep learning concepts clearly for providing the solution.

автор: Richard B

Jun 04, 2020

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

автор: Siladittya M

Jul 24, 2020

Very Good course. Learnt a lot from this course. Also got good hands-on experience.

автор: Branly L

May 15, 2020

Excellent work from the teachers, thanks for your efforts.

автор: Sanchit K

Apr 04, 2020

Please labs are not so good. Please improve it.

автор: Anas O

Jun 13, 2020

Thanks Dr. Alex, I always love your courses

автор: Amine M B

May 09, 2020

Very interesting and helpful

автор: Suprakash S

May 11, 2020

Excellent course!

автор: Krish g

Jun 04, 2020

Marvelous course

автор: Julien V

Jun 03, 2020

Great course !

автор: Christos

Feb 25, 2020

Challenging!!

автор: Nanang K

Jul 26, 2020

Noce project

автор: Carlos F C d S e S

Mar 26, 2020

Thank you!

автор: Alvaro A B A

Apr 06, 2020

Excelent

автор: 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

автор: Meenal I

Jul 17, 2020

The course was good, but the only reason I gave it a 4* is because try as I might, the model fitting kept running out of memory on the provided system. I had to create an account on AWS to get my model to run. Maybe a consideration would be to try an alternate dataset that may fit in memory. I spent over 5 days trying it on IBM till before I had to move. to AWS. It was a great set of courses. Could have been a little more challenging as well.