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Understanding Deepfakes with Keras(으)로 돌아가기

Coursera Project Network의 Understanding Deepfakes with Keras 학습자 리뷰 및 피드백

4.4
별점
145개의 평가
•
20개의 리뷰

강좌 소개

In this 2-hour long project-based course, you will learn to implement DCGAN or Deep Convolutional Generative Adversarial Network, and you will train the network to generate realistic looking synthesized images. The term Deepfake is typically associated with synthetic data generated by Neural Networks which is similar to real-world, observed data - often with synthesized images, videos or audio. Through this hands-on project, we will go through the details of how such a network is structured, trained, and will ultimately generate synthetic images similar to hand-written digit 0 from the MNIST dataset. Since this is a practical, project-based course, you will need to have a theoretical understanding of Neural Networks, Convolutional Neural Networks, and optimization algorithms like Gradient Descent. We will focus on the practical aspect of implementing and training DCGAN, but not too much on the theoretical aspect. You will also need some prior experience with Python programming. 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. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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....

최상위 리뷰

RB
2020년 4월 22일

I had a very nice experience taking this project .The instructor simplifies the concepts and makes them easy to understand and a very nice introduction of Generative Adversarial Networks.

PT
2020년 5월 29일

This really helped me a lot. One should definitely try his (Amit Yadav) projects. Actually, all of it. Gonna be exploring more. I really loved it.

필터링 기준:

Understanding Deepfakes with Keras의 20개 리뷰 중 1~20

교육 기관: Ravi P B

•
2020년 4월 23일

I had a very nice experience taking this project .The instructor simplifies the concepts and makes them easy to understand and a very nice introduction of Generative Adversarial Networks.

교육 기관: Padam J T

•
2020년 5월 30일

This really helped me a lot. One should definitely try his (Amit Yadav) projects. Actually, all of it. Gonna be exploring more. I really loved it.

교육 기관: Deeksha N

•
2020년 10월 18일

Its really helpful to start from here, I got some insights about how to proceed further.

교육 기관: Pratikshya M

•
2020년 11월 6일

Learnt DCGANS, DeepFakes

교육 기관: Gangone R

•
2020년 7월 3일

very useful course

교육 기관: Rishabh R

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2020년 5월 10일

Ecellent project

교육 기관: Doss D

•
2020년 6월 14일

Thank u

교육 기관: Kamlesh C

•
2020년 6월 24일

Thanks

교육 기관: Gaurav S

•
2020년 6월 26일

Good

교육 기관: p s

•
2020년 6월 23일

Nice

교육 기관: sarithanakkala

•
2020년 6월 23일

Good

교육 기관: Abhinav K

•
2020년 4월 26일

Very good course and way of explaining stuff. Technically from the scratch. Maybe inclusion of explanation of why the selected layers are selected on the first place.

교육 기관: BHATT K J

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2020년 4월 18일

Overall good course, but it need to improve online cloud platform.

교육 기관: TANMAY A

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2020년 4월 27일

The project is good enough to give you a start with DCGANs.

교육 기관: daniel s

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2021년 3월 15일

Project is in depth and well informative

교육 기관: avithal e l

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2020년 6월 11일

was compact and on point

교육 기관: Sachin S

•
2020년 9월 24일

it's good

교육 기관: Horace C

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2020년 8월 29일

The speed of virtual machine is too slow; thus, it's highly recommended that the ihands-on lab can be performed by google colab. Thank you.

교육 기관: Mohammadali J

•
2020년 7월 15일

just understand? not learn?

교육 기관: Simon S R

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2020년 8월 31일

Too short, does not go into essential details

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