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学生对 Coursera Project Network 提供的 Neural Style Transfer with TensorFlow 的评价和反馈

4.6
56 个评分
9 条评论

课程概述

In this 2-hour long project-based course, you will learn the basics of Neural Style Transfer with TensorFlow. Neural Style Transfer is a technique to apply stylistic features of a Style image onto a Content image while retaining the Content's overall structure and complex features. We will see how to create content and style models, compute content and style costs and ultimately run a training loop to optimize a proposed image which retains content features while imparting stylistic features from another image. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Tensorflow pre-installed. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions....

热门审阅

RB

Jun 18, 2020

Excellent and precise explanation.Nice course.Instructor has been really fantastic.

PA

Jun 03, 2020

This was a great project. Explanations were given nicely.

筛选依据:

1 - Neural Style Transfer with TensorFlow 的 9 个评论(共 9 个)

创建者 Ravi P B

Jun 18, 2020

Excellent and precise explanation.Nice course.Instructor has been really fantastic.

创建者 Prasanna R A

Jun 03, 2020

This was a great project. Explanations were given nicely.

创建者 Prashik R

Jul 03, 2020

nice explanation by amit sir

创建者 Ashwani Y

May 21, 2020

simply awesome

创建者 KHOKHRIYA D

Apr 11, 2020

Great Learning

创建者 JONNALA S R

May 07, 2020

Good Analysis

创建者 tale p

Jun 28, 2020

good

创建者 Rajasinghe R

May 28, 2020

good

创建者 Abrar I A

Apr 24, 2020

I will give 4.6/5 the project.This project gives me the opportunity to learn my long desired NST work.Now i am able to generate my own desired content and style image.Though some theoretical content could be handy here still it was a good project to finish.