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Отзывы учащихся о курсе Understanding Deepfakes with Keras от партнера Coursera Project Network

4.4
звезд
Оценки: 139
Рецензии: 19

О курсе

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....

Лучшие рецензии

RB
22 апр. 2020 г.

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 мая 2020 г.

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.

Фильтр по:

1–19 из 19 отзывов о курсе Understanding Deepfakes with Keras

автор: Ravi P B

23 апр. 2020 г.

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.

автор: Padam J T

30 мая 2020 г.

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.

автор: Deeksha N

18 окт. 2020 г.

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

автор: Pratikshya M

6 нояб. 2020 г.

Learnt DCGANS, DeepFakes

автор: Gangone R

3 июля 2020 г.

very useful course

автор: Rishabh R

10 мая 2020 г.

Ecellent project

автор: Doss D

14 июня 2020 г.

Thank u

автор: Kamlesh C

24 июня 2020 г.

Thanks

автор: Gaurav S

26 июня 2020 г.

Good

автор: p s

23 июня 2020 г.

Nice

автор: sarithanakkala

23 июня 2020 г.

Good

автор: Abhinav K

26 апр. 2020 г.

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.

автор: BHATT K J

18 апр. 2020 г.

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

автор: TANMAY A

27 апр. 2020 г.

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

автор: avithal e l

11 июня 2020 г.

was compact and on point

автор: Sachin S

24 сент. 2020 г.

it's good

автор: CSIE, E I P

29 авг. 2020 г.

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.

автор: Mohammadali J

15 июля 2020 г.

just understand? not learn?

автор: Simon S R

31 авг. 2020 г.

Too short, does not go into essential details