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Udemy – Computer Vision: Python Face Swap & Quick Deepfake in Colab 2020-9

Udemy – Computer Vision: Python Face Swap & Quick Deepfake in Colab 2020-9

Published on: 2021-05-13 17:59:31

Categories: 28

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Description

Computer Vision: Python Face Swap & Quick Deepfake in Colab is a face-swapping training course using Python and OpenCV and Deepfike image animation using the First Order Motion Model in Colab. There is an old saying that seeing is believing, but in the world of Deep Fic, everything we see is not necessarily true.

In the first half of the course, we will build a Python-based face exchange program, and before that, we will have an introduction to deep fake techniques, their advantages and disadvantages. We will then prepare our system requirements and install Anaconda, our Python programming development platform and environment, and provide optional Python introductory training sessions. In the following, we will become more familiar with Deep Fick by using an animation model. Of course, since working with Deep Fick requires expensive GPUs, we will use the alternative and free way of Google Colab.

What you will learn in Computer Vision: Python Face Swap & Quick Deepfake in Colab:

Course specifications

Publisher: Udemy
Instructors: Abhilash Nelson
Language: English
Level: Introductory to Advanced
Number of Courses: 28
Duration: 4 hours and 5 minutes

Computer Vision Course Titles: Python Face Swap & Quick Deepfake in Colab 2020-12:

Computer Vision Python Face Swap Quick Deepfake in Colab Contents

Prerequisites course:

A decent computer configuration (preferably Windows) and an enthusiasm to research with Deepfake Technology

Pictures

Computer Vision Python Face Swap Quick Deepfake in Colab

Sample film

Installation guide

After Extract, watch with your favorite Player.

Subtitle: No (based on the course specifications in the image, this course has no subtitles.)

Quality: 720

download link

Download Part 1 – 1 GB

Download Part 2 – 1 GB

Download Part 3 – 600 MB

File password (s): www.abc.com

Size

2.6 GB

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