Understanding Deepfakes with Keras

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В этом Проект с консультациями вы:

Implement a Deep Convolutional Generative Adversarial Network (DCGAN).

Train a DCGAN to synthesize realistic looking images.

Clock2 hours
AdvancedПродвинутые функции
CloudЗагрузка не требуется
VideoВидео на разделенном экране
Comment DotsАнглийский
LaptopТолько для ПК

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.

Навыки, которые вы получите

Deep LearningdeepfakesGANMachine Learningkeras

Будете учиться пошагово

На видео, которое откроется рядом с рабочей областью, преподаватель объяснит эти шаги:

  1. Introduction

  2. Importing and Plotting the Data

  3. Discriminator

  4. Generator

  5. Generative Adversarial Network

  6. Training the GAN

  7. Final Results

Как устроены проекты с консультациями

Ваше рабочее пространство — это облачный рабочий стол в браузере. Ничего не нужно загружать.

На разделенном экране видео преподаватель предоставляет пошаговые

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