With advances in camera quality, image fidelity, and neural network research focused on solving image- and video-based challenges, computer vision continues to capture the attention and imaginations of machine learning researchers and practitioners.
But computer vision is an incredibly broad umbrella term that encompasses an array of specific tasks and challenges, and the field continues to expand.
Continue reading “Machine Learning for Computer Vision: Foundations and Use Cases”
Mobile machine learning is still, relatively speaking, in its infancy. As such, new developer tools, frameworks, and environments are regularly in flux.
It can be hard to keep up with the rapid advancements happening at this emerging intersection.
Continue reading “Mobile Machine Learning: Development Tools and Frameworks”
Pneumonia is an inflammatory condition of the lung affecting the alveoli. Typically, symptoms include some combination of productive or dry cough, chest pain, fever, and trouble breathing. According to UNICEF pneumonia remains the leading infectious cause of death among children under five, killing approximately 2,400 children a day.
Early diagnosis of this treatable condition is very important, as it could facilitate earlier treatment and hopefully result in improved clinical outcomes. There are several tests used to confirm diagnoses, including analysis of chest X-ray images.
Continue reading “Detecting Pneumonia in an iOS App with Create ML”
Imagine we have a set of labeled and unlabeled data, and we want to build a classifier which takes the unlabeled data as input and labels that data as output.
With this kind of situation, we’ll need to build a classification model that will learn from already-labeled data (training data). Later we’ll use that model to predict our unlabeled data (test data).
This type of machine learning is called supervised learning, which we can define as feeding data into a machine learning algorithm.
In doing so, we’re actually showing that groups exist, and which data belong to which groups.
Continue reading “Implementing K-Nearest Neighbors in Your Machine Learning Model”