The loss function is the bread and butter of modern machine learning; it takes your algorithm from theoretical to practical and transforms neural networks from glorified matrix multiplication into deep learning.
This post will explain the role of loss functions and how they work, while surveying a few of the most popular from the past decade.
Many of the exciting applications in Machine Learning have to do with images, which means they’re likely built using Convolutional Neural Networks (or CNNs). This type of algorithm has been shown to achieve impressive results in many computer vision tasks and is a must-have part of any developer’s or data scientist’s modern toolkit.
This tutorial will walk through the basics of the architecture of a Convolutional Neural Network (CNN), explain why it works as well as it does, and step through the necessary code piece by piece. You should finish this with a good starting point for developing your own more complex architecture and applying CNNs to problems that intrigue you.
What do you do when your dataset doesn’t have any labels? Unsupervised learning is a group of machine learning algorithms and approaches that work with this kind of “no-ground-truth” data. This post will walk through what unsupervised learning is, how it’s different than most machine learning, some challenges with implementation, and resources for further reading.
Using software to parse the world’s visual content is as big of a revolution in computing as mobile was 10 years ago, and will provide a major edge for developers and businesses to build amazing products.
Computer Vision is the process of using machines to understand and analyze imagery (both photos and videos). While these types of algorithms have been around in various forms since the 1960’s, recent advances in Machine Learning, as well as leaps forward in data storage, computing capabilities, and cheap high-quality input devices, have driven major improvements in how well our software can explore this kind of content. Read More…
One of the most compelling use cases of sentiment analysis today is brand awareness. If you can understand what people are saying about you in a natural context, you can work towards addressing key problems and improving your business processes. So how exactly can you get that up and running?
The Algorithmia marketplace makes it easy to extract the content you need from Twitter and pipe it into the right algorithms for sentiment analysis. There are a few algorithms on the platform for exploring different information from Twitter (like users, tweets, and followers), and a number for sentiment analysis.