Face recognition is the problem of identifying and verifying people in a photograph by their face. It is a task that is trivially performed by humans, even under varying light and when faces are changed by age or obstructed with accessories and facial hair. Nevertheless, it is remained a challenging computer vision problem for decades […]
Search results for "Digital Transformation"
Deep Learning for Computer Vision Image Classification, Object Detection, and Face Recognition in Python …why deep learning? Traditionally, Computer Vision is REALLY HARD We are awash in images: photographs, videos, YouTube, Instagram, and increasingly from live video. Computer Vision, often shortened to CV, is defined as a field of study that seeks to develop techniques […]
Computer vision is a subfield of artificial intelligence concerned with understanding the content of digital images, such as photographs and videos. Deep learning has made impressive inroads on challenging computer vision tasks and makes the promise of further advances. Before diving into the application of deep learning techniques to computer vision, it may be helpful […]
Statistics and machine learning are two very closely related fields. In fact, the line between the two can be very fuzzy at times. Nevertheless, there are methods that clearly belong to the field of statistics that are not only useful, but invaluable when working on a machine learning project. It would be fair to say […]
Transduction or transductive learning are terms you may come across in applied machine learning. The term is being used with some applications of recurrent neural networks on sequence prediction problems, like some problems in the domain of natural language processing. In this post, you will discover what transduction is in machine learning. After reading this […]
Recurrent neural networks, or RNNs, are a type of artificial neural network that add additional weights to the network to create cycles in the network graph in an effort to maintain an internal state. The promise of adding state to neural networks is that they will be able to explicitly learn and exploit context in […]