In this tutorial, you will learn how to perform liveness detection with OpenCV. You will create a liveness detector capable of spotting fake faces and performing anti-face spoofing in face recognition systems. How do I spot real versus fake faces? Consider what would happen if a nefarious user tried to purposely circumvent your face recognition system. Such a user could try to hold up a photo of another person. Maybe they even have a photo or video on their smartphone that they could hold up to the camera responsible for performing face recognition (such as in the image at the top of this post).
Convolution and the convolutional layer are the major building blocks used in convolutional neural networks. A convolution is the simple application of a filter to an input that results in an activation. Repeated application of the same filter to an input results in a map of activations called a feature map, indicating the locations and strength of a detected feature in an input, such as an image. The innovation of convolutional neural networks is the ability to automatically learn a large number of filters in parallel specific to a training dataset under the constraints of a specific predictive modeling problem, such as image classification. The result is highly specific features that can be detected anywhere on input images. In this tutorial, you will discover how convolutions work in the convolutional neural network.
Computational neuroscientist Sarah Schwettmann is one of three instructors behind the cross-disciplinary course 9.S52/9.S916 (Vision in Art and Neuroscience), which introduces students to core concepts in visual perception through the lenses of art and neuroscience. Supported by a faculty grant from the Center for Art, Science and Technology at MIT (CAST) for the past two years, the class is led by Pawan Sinha, a professor of vision and computational neuroscience in the Department of Brain and Cognitive Sciences. They are joined in the course by Seth Riskin SM '89, a light artist and the manager of the MIT Museum Studio and Compton Gallery, where the course is taught. Schwettman discussed the combination of art and science in an educational setting. Q: How have the three of you approached this cross-disciplinary class in art and neuroscience?
The smartest companies now approach cybersecurity with a risk management strategy. Learn how to make policies to protect your most important digital assets. The Royal Melbourne Institute of Technology (RMIT) has announced a new online course on cybersecurity in a bid to address Australia's cybersecurity skills shortage. As part of the course, RMIT Online has partnered with the National Australia Bank (NAB) and Palo Alto Networks, with both organisations to provide mentors for the course. The course, called Cyber Security Risk and Strategy, will cover topics such as the fundamentals of cybersecurity and how to apply cybersecurity risk mitigation strategies to an organisation.
Students are expected to be self-motivated, curious and enthusiastic about machine learning on graphs. You'll get the most out of this course by completing the (moderate time commitment) coursework, so make sure you have the free time and energy needed for that. The course will have lectures every two weeks, for four lectures, taking a total of two months to complete. Each two week cycle will begin with an interactive online lecture in a Google Hangout. This will be followed with a piece of coursework, provided as a template Google Colab notebook. A week after the lecture will be a tutorial session, where the students and tutor(s) will get together and discuss the coursework and what challenges students are having.
Artificial Intelligence may be the answer to a great set of issues that are apparent worldwide. AI would reform the human's daily life and bring new solutions to many industries. AI is deeply penetrating into the daily lives of people but still, there is a lot of untapped potential of Artificial Intelligence, especially towards the humanitarian cause. Scientist and AI experts are working to help solve some of the most important social and economic issues of our day. One of the most common problems for developing countries is to tackle poverty and AI can play a vital role.
This course is about the fundamental concepts of artificial intelligence. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detecting cancer for example. We may construct algorithms that can have a very good guess about stock price movement in the market. In the first chapter we are going to talk about the basic graph algorithms.
It can be convenient to use a standard computer vision dataset when getting started with deep learning methods for computer vision. Standard datasets are often well understood, small, and easy to load. They can provide the basis for testing techniques and reproducing results in order to build confidence with libraries and methods. In this tutorial, you will discover the standard computer vision datasets provided with the Keras deep learning library. How to Load and Visualize Standard Computer Vision Datasets With Keras Photo by Marina del Castell, some rights reserved.
News flash: people are still getting smarter (really). On one hand, there's the Flynn effect, which pertains to the pattern of human IQ consistently rising year after year. But you can also consider this: In the next century, brain hacks will be the stuff of today's science fiction. We may well take a pill to enhance brain capacity, or wear a headband to rev up those neural connections. Elon Musk even suggested we will have creepy cranial bionic implants to boost intelligence, thus making the term "brain hacks" quite literal (um, no thank you).