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Avis et commentaires pour d'étudiants pour Understanding Deepfakes with Keras par Coursera Project Network

4.4
étoiles
152 évaluations
•
21 avis

À propos du cours

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....

Meilleurs avis

RB

22 avr. 2020

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

29 mai 2020

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.

Filtrer par :

1 - 21 sur 21 Avis pour Understanding Deepfakes with Keras

par Ravi P B

•

23 avr. 2020

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.

par lonnie

•

17 avr. 2021

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.

par Padam J T

•

30 mai 2020

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.

par Deeksha N

•

18 oct. 2020

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

par Pratikshya M

•

6 nov. 2020

Learnt DCGANS, DeepFakes

par Gangone R

•

3 juil. 2020

very useful course

par Rishabh R

•

10 mai 2020

Ecellent project

par Doss D

•

14 juin 2020

Thank u

par Kamlesh C

•

24 juin 2020

Thanks

par Gaurav S

•

26 juin 2020

Good

par p s

•

23 juin 2020

Nice

par sarithanakkala

•

23 juin 2020

Good

par Abhinav K

•

26 avr. 2020

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.

par BHATT K J

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18 avr. 2020

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

par TANMAY A

•

27 avr. 2020

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

par daniel s

•

15 mars 2021

Project is in depth and well informative

par avithal e

•

11 juin 2020

was compact and on point

par Sachin S

•

24 sept. 2020

it's good

par Horace C

•

29 août 2020

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.

par Mohammadali J

•

15 juil. 2020

just understand? not learn?

par Simon S R

•

31 août 2020

Too short, does not go into essential details

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