Understanding Deepfakes with Keras
6.947 já se inscreveram
6.947 já se inscreveram
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.
Em um vídeo reproduzido em uma tela dividida com a área de trabalho, seu instrutor o orientará sobre esses passos:
Sua área de trabalho é um espaço em nuvem, acessado diretamente do navegador, sem necessidade de nenhum download
Em um vídeo de tela dividida, seu instrutor te orientará passo a passo
por TA26 de abr de 2020
The project is good enough to give you a start with DCGANs.
por BJ17 de abr de 2020
Overall good course, but it need to improve online cloud platform.
por AK25 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 LL16 de abr de 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.