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Understanding Deepfakes with Keras に戻る

Coursera Project Network による Understanding Deepfakes with Keras の受講者のレビューおよびフィードバック

4.4
149件の評価
21件のレビュー

コースについて

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....

人気のレビュー

RB
2020年4月22日

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.

LL
2021年4月16日

This course is very excellent and efficient, it helps me understand GANs just in 1 hours. Before although I read many articles about GANs, I still was very confused about it.

フィルター:

Understanding Deepfakes with Keras: 1 - 21 / 21 レビュー

by Ravi P B

2020年4月23日

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.

by lonnie

2021年4月17日

This course is very excellent and efficient, it helps me understand GANs just in 1 hours. Before although I read many articles about GANs, I still was very confused about it.

by Padam J T

2020年5月30日

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.

by Deeksha N

2020年10月18日

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

by Pratikshya M

2020年11月6日

Learnt DCGANS, DeepFakes

by Gangone R

2020年7月3日

very useful course

by Rishabh R

2020年5月10日

Ecellent project

by Doss D

2020年6月14日

Thank u

by Kamlesh C

2020年6月24日

Thanks

by Gaurav S

2020年6月26日

Good

by p s

2020年6月23日

Nice

by sarithanakkala

2020年6月23日

Good

by Abhinav K

2020年4月26日

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.

by BHATT K J

2020年4月18日

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

by TANMAY A

2020年4月27日

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

by daniel s

2021年3月15日

Project is in depth and well informative

by avithal e

2020年6月11日

was compact and on point

by Sachin S

2020年9月24日

it's good

by Horace C

2020年8月29日

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.

by Mohammadali J

2020年7月15日

just understand? not learn?

by Simon S R

2020年8月31日

Too short, does not go into essential details