Self driving cars thought to be a distant dream just a few decades ago. However, thanks to the recent progress made in various fields of computer science, this dream is becoming a reality now. Computer vision plays a central role in understanding the capabilities these vehicles required to be able to operate not only under standard conditions, but also under the most unexpected situations. Machine Learning is everywhere these days. We live in a world where Machine Learning and Artificial Intelligence is not obscure mathematical and science fiction anymore they have become crucial part of our lives.
OpenCV is a native cross-platform C library for Computer Vision, Machine Learning, and image processing. It is increasingly being adopted for development in Python. This course features some trending applications of vision and deep learning and will help you master these techniques. You will learn how to retrieve structure from motion (sfm) and you will also see how we can build an application to capture 2D images and join them dynamically to achieve street views by capturing camera projection angles and relative image positions. You will also learn how to track your head in 3D in real-time, and perform facial recognition against a goldenset.
Prime Minister Justin Trudeau reaffirmed his nerd-in-chief reputation and outlined his government's vision to capitalize on Canada's early lead in artificial intelligence, or AI, during an appearance today at the University of Toronto's Rotman School of Management. Trudeau, a self-professed "geek," was a special guest at an annual business of AI conference hosted by Rotman's Creative Destruction Lab (CDL), a seed stage accelerator that specializes in building AI-powered startups. "I think we all understand, certainly in this room, the way the world is going," Trudeau said during a 20-minute conversation with Shivon Zilis of Tesla, Bloomberg Beta and Open AI. "So let's be part of it and help shape it, and let's make sure we're benefiting from the innovations – in both the designing of them and the applications and the jobs." In recent years, Canada – and Toronto in particular – has emerged as a hotbed of AI activity thanks in part to fundamental research performed by people like U of T's University Professor Emeritus Geoffrey Hinton, who is known as the "godfather of deep learning" and works for Google, and U of T Associate Professor Raquel Urtasun, who is heading up Uber's self-driving car lab in Toronto. In a bid to capitalize on the country's early lead that is expected to transform everything from transportation to medicine, the Trudeau government announced in March that it would make a $125 million investment in a pan-Canadian AI strategy.
Geoffrey Hinton may be the "godfather" of deep learning, a suddenly hot field of artificial intelligence, or AI – but that doesn't mean he's resting on his algorithms. Hinton, a University Professor Emeritus at the University of Toronto, recently released two new papers that promise to improve the way machines understand the world through images or video – a technology with applications ranging from self-driving cars to making medical diagnoses. "This is a much more robust way to detect objects than what we have at present," Hinton, who is also a fellow at Google's AI research arm, said today at a tech conference in Toronto. "If you've been in the field for a long time like I have, you know that the neural nets that we use now – there's nothing special about them. We just sort of made them up."
About this course: Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars. The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation.