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学生对 英特尔 提供的 An Introduction to Practical Deep Learning 的评价和反馈

4.3
95 个评分
20 条评论

课程概述

This course provides an introduction to Deep Learning, a field that aims to harness the enormous amounts of data that we are surrounded by with artificial neural networks, allowing for the development of self-driving cars, speech interfaces, genomic sequence analysis and algorithmic trading. You will explore important concepts in Deep Learning, train deep networks using Intel Nervana Neon, apply Deep Learning to various applications and explore new and emerging Deep Learning topics....

热门审阅

AB

Oct 11, 2019

I thank Coursera and the instructors for guiding through the basics of Deep Learning. The explanations were easy to understand, and would recommend this course to enthusiasts.

SR

Jun 30, 2020

This course was very helpful to understand practical application and training on Deep Learning

筛选依据:

1 - An Introduction to Practical Deep Learning 的 21 个评论(共 21 个)

创建者 Robert G

Nov 07, 2019

Exercises do not work. Nervana is deprecated.

创建者 Ramakrishna B C

Apr 05, 2019

This course is not worth the time and money. I would suggest doing any of courses in the Andrew Ng's deeplearning.ai specialization instead. No programming assignments that's evaluated here. No instructor or TA are active in the forums. No questions are answered in the discussion forums. Many courses in deep learning are available online where you can actually learn something by getting hands-on.

创建者 Praveen k

Apr 28, 2019

No projects! The knowledge level is good though.

创建者 Emil L

Sep 25, 2018

A very dense and informative course.

创建者 Raveesh G

Jul 07, 2019

The theoretical aspect is good for those aiming at basics but the neon exercises are average. The data sets should be readily imported or there should at least be one standard method to import any kind of data set. Except for this part, the exercises complemented the videos.

创建者 Shubham r

Oct 20, 2018

Good for theory knowledge , but very low in practical knowledge

创建者 Annadurai

Jul 12, 2020

It is a challenge for me to devote time and attend the course in spite of the administrative works. I had a different experience in learning. All the lectures were well presented and with quality.

创建者 Aditya B

Oct 11, 2019

I thank Coursera and the instructors for guiding through the basics of Deep Learning. The explanations were easy to understand, and would recommend this course to enthusiasts.

创建者 Sneha R

Jun 30, 2020

This course was very helpful to understand practical application and training on Deep Learning

创建者 Ehtesham H

Feb 13, 2019

Excellent learning experience. Thank you...

创建者 B B B

Oct 09, 2019

Very good course with practical aspects

创建者 RAMA R R

Apr 01, 2019

Thank you Intel and Coursera

创建者 Brad C

May 13, 2018

Love this course!! Thanks.

创建者 Anirban L

Aug 09, 2019

Awesome experience

创建者 Mohammed S E

Feb 22, 2019

well it has been a good course for me to get an overall view on Deep learning , but there was so much information in every video that was hard to grasp from the first time

创建者 Gunjan B

Jun 24, 2019

Tough Enough To Remove Dust Of Time From Your Brain. Quizes Were Good Brain Exercise

创建者 Dongliang Y

Jul 30, 2018

Great course

创建者 PIYUSH G

Apr 20, 2020

GOOD COURSE

创建者 Ambar R

Mar 26, 2019

very good

创建者 Cynthia K

Jun 24, 2020

While the material was fine, there were some issues with this course. Many of the exercises would not work because they were outdated or had files missing. Critical information, such as formulas for solving problems, was not presented in a way that let you know it was important. Because of this, I found myself backtracking and scouring through videos to find what I was missing. This course does not feel like an "introduction" class; it seems like there should be a pre-requisite to it, or at least a list of what-you-will-need before taking it.

创建者 Niranjan S

Jun 08, 2020

The homework is not very explanatory. And there should be more activity in the forums. The course lectures are great for getting an overview of DNN and CNN. But there should be more details and more explanatory assignments.