Machine Learning

AI learning evolution

Contact with ExpertsGitHub Repositories
A report that seeks to trigger informed conversation about the state of AI and its implication for the future.
Introduction When we're shown an image, our brain instantly recognizes the objects contained in it. But it takes a lot of time and training data for a machine to identify these objects. But with the recent advances in hardware and deep learning, this computer vision field has become a whole lot easier and more intuitive.
Domain-specific hands-on tutorials that teach how to use machine learning to solve real-world problems.
Learn more about the University of Helsinki and Reaktor's upcoming AI course for students and business professionals - no programming or math skills required.
A visual introduction to machine learning-Part II The goal of modeling is to approximate real-life situations by identifying and encoding patterns in data. Models make mistakes if those patterns are overly simple or overly complex. In Part 1, we created a model that distinguishes homes in San Francisco from those in New York.
Object detection is a domain that has benefited immensely from the recent developments in deep learning. Recent years have seen people develop many algorithms for object detection, some of which include YOLO, SSD, Mask RCNN and RetinaNet. For the past few months, I've been working on improving object detection at a research lab.
Tom Silver | About Me | Favorite Papers | Blog By Tom Silver A friend of mine who is about to start a career in artificial intelligence research recently asked what I wish I had known when I started two years ago. Below are some lessons I have learned so far.