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Opiniones y comentarios de aprendices correspondientes a Understanding Deepfakes with Keras por parte de Coursera Project Network

4.4
estrellas
139 calificaciones
19 reseña

Acerca del Curso

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....

Principales reseñas

RB
22 de abr. de 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 de may. de 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.

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1 - 19 de 19 revisiones para Understanding Deepfakes with Keras

por Ravi P B

23 de abr. de 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.

por Padam J T

30 de may. de 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.

por Deeksha N

18 de oct. de 2020

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

por Pratikshya M

6 de nov. de 2020

Learnt DCGANS, DeepFakes

por Gangone R

3 de jul. de 2020

very useful course

por Rishabh R

10 de may. de 2020

Ecellent project

por Doss D

14 de jun. de 2020

Thank u

por Kamlesh C

24 de jun. de 2020

Thanks

por Gaurav S

26 de jun. de 2020

Good

por p s

23 de jun. de 2020

Nice

por sarithanakkala

23 de jun. de 2020

Good

por Abhinav K

26 de abr. de 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.

por BHATT K J

18 de abr. de 2020

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

por TANMAY A

27 de abr. de 2020

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

por avithal e l

11 de jun. de 2020

was compact and on point

por Sachin S

24 de sep. de 2020

it's good

por CSIE, E I P

29 de ago. de 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.

por Mohammadali J

15 de jul. de 2020

just understand? not learn?

por Simon S R

31 de ago. de 2020

Too short, does not go into essential details