There are 17 posts filed in machine learning ( this is page 1 of 2).
You can read all the posts in this series here:
You can read all the posts in this series here:
You can read all the posts in this series here:
You can read all the posts in this series here:
You can read all the posts in this series here:
This online course provides a theoretical and practical guide to the use of face-swap technology. In the past few months, deep neural networks have been wildly used to digital insert actor Nicolas Cage into several movie scenes. These so-called deepfakes have generated a lot of discussion on the ethics of Machine Learning. This second lesson will focus on the potential applications that face-swap technology can offer, and on how to use it properly.
If you are interested in understanding not only how deekfakes are generated, but also to create your own, this is the tutorial you have been looking for. And if you have been using face-swap technology already, I hope this first post will help you become more aware of why and how this technology should (and shouldn’t) be used.
You can read all the posts in this series here:
This tutorial will cover the theory and practice of creating deepfakes: videos in which faces have been swapped using Machine Learning and Deep Neural Networks. If you are interested in learning more about this novel technique, this is the course for you.
After a theoretical introduction, this course will focus on how to make the most out of the popular applications FakeApp and faceswap; most of the deepfakes you will find online (such as the ones featuring Nicolas Cage) have been created using them.
You can read all the posts in this series here:
Noise is everywhere. Whether you’re sampling accelerometer data for a mobile game or trying to measure the temperature of a room, noise will be there. Even if you could remove all the noise from an input device, you’ll still have a certain degree of uncertainty. If a player has tapped on the screen, where did they really wanted to tap? All these scenarios forces to re-think about how we gather and preprocess data.
The previous post in this series, Understanding Deep Dreams, explained what deep dreams are, and what they can be used for. In this second post you’ll learn how to create them, with a step by step guide.
In the last few months the Internet has been flooded with deep dreams: images augmented by neural networks which look incredibly trippy. Deep dreams have the potential to become the new fractals; beautifully backgrounds everyone knows are related to Maths, but no one knows really how. What are deep dreams, how are they generated and what can they teach us?
