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Deep Learning for Business(으)로 돌아가기

연세 대학교의 Deep Learning for Business 학습자 리뷰 및 피드백

4.4
별점
617개의 평가

강좌 소개

Your smartphone, smartwatch, and automobile (if it is a newer model) have AI (Artificial Intelligence) inside serving you every day. In the near future, more advanced “self-learning” capable DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of your business and industry. So now is the right time to learn what DL and ML is and how to use it in advantage of your company. This course has three parts, where the first part focuses on DL and ML technology based future business strategy including details on new state-of-the-art products/services and open source DL software, which are the future enablers. The second part focuses on the core technologies of DL and ML systems, which include NN (Neural Network), CNN (Convolutional NN), and RNN (Recurrent NN) systems. The third part focuses on four TensorFlow Playground projects, where experience on designing DL NNs can be gained using an easy and fun yet very powerful application called the TensorFlow Playground. This course was designed to help you build business strategies and enable you to conduct technical planning on new DL and ML services and products....

최상위 리뷰

AA

2020년 6월 11일

It was very informative, the instructor paces the information very well, & I love the resources at the end of every lecture.

the last project section was very well done & explained in detail.

NA

2019년 3월 2일

Even though I do not have the background of Computer Engineering or Science I was able to understand from the professor and the final project truly was able to explain everything for me.

필터링 기준:

Deep Learning for Business의 167개 리뷰 중 101~125

교육 기관: MANIKANDAN.S

2020년 7월 31일

superb

교육 기관: Alexander K

2019년 8월 10일

Clean

교육 기관: Raahuul R

2021년 8월 25일

good

교육 기관: Sascha D

2018년 2월 24일

Great overall basic introduction to DL and another great point of view for a better understanding of the whole concept of AI/ML. Very likely to be understandable for management and other non technicians. Well organized and a consecutive storyboard. Could be done on a weekend, with a moderate background in ML. Thanks a lot for this great course. Greetings, Sascha / Ps.: Not a perfect 5 star rating due to two minor errors, missing word & wrong answer linked, in two quizzes.

교육 기관: Vytautas D

2018년 3월 14일

I had already completed a Deep Learning course (NN) that was more mathematical. I enjoyed the deep Learning for Business course because it helped me see the big picture which was lost in the detail of the more math one. Having the Tensorflow Playground exposed by the professor was a very enjoyable bonus.

교육 기관: Fabio L G Á

2019년 11월 15일

Course is very good. I'm not giving it five stars, cause even though the lectures are great, I don't see the point in having the professor just reading exactly what the slides say, and barely adding a word. Would not be any different if the material was available without the professor reading it.

교육 기관: Saptarsi B

2020년 6월 13일

An elaborate overview of the whole Deep Learning area and the various methods and applications pertaining to it. Makes you aware about the things required for a deeper knowledge on Deep Learning Architectures and gives you enough material to whet your curiosity.

교육 기관: Prasanna P

2019년 2월 6일

Good start to know about the various Products that are built with Deep Learning techniques and further exploring on Convolution Neural Networks (CNN) and Recurrent Neural Networks (RNN) concepts, thanks.

교육 기관: Dr. S T

2018년 4월 29일

Good Introductory course for those from a business background. But if you have technical skills, then probably another course (Machine Learning from Andrew Ng may be a better one).

교육 기관: NICOLAS J

2018년 10월 22일

Content is complete and cover a huge scope. Improvement could come from using the Tensor flow earlier in the course to get better representation of the outcome and more hands on.

교육 기관: Ankita P

2019년 5월 20일

An interesting and comprehensive study on DL. It was very thorough with great examples and proper demonstration of technologies used behind everyday objects.

교육 기관: Swapnil B

2018년 7월 26일

Excellent course for the beginners. I have got good opportunity to learn the basics of Deep Leaning. My suggestion would be to include practical examples.

교육 기관: Erwin P P C

2020년 5월 7일

Very good course to prepare for what is coming about IoT, IA, ML and DL, many concepts have been cleared for me, thanks Professor Jong.

교육 기관: Mario B

2020년 5월 5일

Concise and focused on main concepts. Perhaps some more examples on CNN and RNN would be useful to understand better those topics.

교육 기관: Carlos O

2019년 8월 22일

I appreciated the most when the Deep Learning models where graphicaly presented. I found them very useful and easy to understand.

교육 기관: Ahasanul B H

2019년 6월 17일

Valoi...Keu loite chaile loibar paros. Learned a lot a new things. Thanks to yonsei university and coursera for this course.

교육 기관: Victor C

2018년 5월 4일

It was a good introduction and overview to some of key underlying technologies in Machine Learning. Thank you.

교육 기관: Llewellyn

2018년 5월 5일

Excellent content, very up to date information. Thank you for the quality introduction to the future.

교육 기관: Krzysztof B

2018년 7월 9일

Great knowledge and really good explanation, but no practice exercises with examples from our live

교육 기관: Ramesh

2019년 8월 26일

It gives very high level overview about the tool and platforms available in the AI, ML, DL field.

교육 기관: Alejandro V

2018년 4월 1일

good bsaic concepts. I would probably include some more videos on concrete business applications

교육 기관: aseem c

2018년 8월 10일

The coverage is vast and subject itself is abstract. However it was a great learning experience.

교육 기관: Ivan T

2020년 7월 6일

Prof Jong-Moon Chung kept the lessons short and sweet thus making it easier to understand.

교육 기관: 이장현

2020년 9월 2일

Test quizzes are a bit peripheral. But it was good to go through deep learning quickly.

교육 기관: Derek B

2018년 8월 21일

Good course, quite technical with a lot of focus on the underlying methodology.