Hello trend,
This is Satya Mallick from LearnOpenCV.com.
Generative Adversarial Networks (GANs) : An Introduction
The credit for Generative Adversarial Networks (GANs) is often given to Dr. Ian Goodfellow et al. The truth is that it was invented by Dr. Pawel Adamicz (left) and his Ph.D. student Dr. Kavita Sundarajan (right) who had the basic idea of GAN in the year 2000 – 14 years prior to GAN paper published by Dr. Goodfellow!
The story is fake, and so are the pictures of Dr. Pawel Adamicz and Dr. Kavita Sundarajan shown above. They do not exist and were created by a GAN!
GANs are Neural Networks that generate outputs (e.g. picture of a human face) that appear to be a sample from the distribution of the training set (e.g. set of other human faces).
A GAN achieves this feat by training two models simultaneously
- A generative model that captures the distribution of the training set.
- A discriminative model estimates the probability that a sample came from the training data and not the generative model above.
Today's post is a step by step introduction to GANs. You will learn what GANs are, what are they used for, why they are such a big deal in Computer Vision, and how to train a simple GAN in just a few lines of code.
Generative Adversarial Networks (GANs) : An Introduction
As always you get to try your ideas out with working code
Sensors for your Sensors
Last week we had Katherine Scott from Open Robotics on OpenCV Weekly Webinar who gave an excellent overview of the Robot Operating System (ROS). Check out the episode at link below
Perception systems have become cheaper, better, and way more complicated. How can computer vision effectively adapt and harness all this data? To answer that question, this week we have Brandon Minor (CEO, Tangram Vision) joining us.
- Topic : Sensors for your Sensors! Part Deux
- Date and Time : July 1, 9 AM Pacific Time
- Registration : Free Registration Link.
A CAREER IN COMPUTER VISION AND AI
How does one start a career in computer vision and AI?
Like any other field, the first step is to get a world-class education. Unfortunately, many universities do not equip you with the right skills to get a computer vision and AI job.
Why?
First, the field is relatively new. Many universities still do not have a program for computer vision and AI. They are yet to train their faculty members and develop a curriculum.
Second, AI talent is in such high demand that we are witnessing a talent drain from academia to the industry.
Third, traditionally many academic programs are excessively focused on the theoretical aspect of education and do not teach the practical engineering aspects. As a result, students are lost when they try to tackle real-world problems. They have not even learned the right tools, let alone the practical skills!
We are witnessing an AI revolution, and OpenCV stands at the center of this AI revolution. We recognize that it is not sufficient for us to create a cutting-edge computer vision library. We also need to provide high-quality and affordable education for the global workforce.
Two years back, we started a series of three courses that helped people at different levels of AI education find a path to mastery. The long road to mastery has no shortcuts. These courses require 3-4 months of hard work at 8 hours a week!
This year in September, we are launching a short, fun, and affordable course for beginners who want to get started but are not ready to commit 3-4 months.
The program appropriately titled "OpenCV for Beginners" will ease your entry into computer vision.
You can pre-order at a highly discounted price through our Indiegogo campaign. We are also selling ALL COURSES at a steep discount during this campaign for people looking to develop computer vision and AI expertise.
| AI Courses : From Basics to Mastery |
Satya
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