Jeff Prosise

Learn what principal component analysis (PCA) is, how it works, and more importantly, how to use it to solve real-world problems, with plenty of code samples to light the way.

My previous post introduced two popular algorithms for detecting faces in photographs: Viola-Jones, which relies on machine learning, and MTCNNs, which rely on deep learning.

My previous post demonstrated how to use transfer learning with a CNN trained on millions of facial images to build a facial-recognition model that is remarkably adept at identifying faces.

Not long ago, I boarded a flight to Europe and was surprised that I didn’t have to show my passport.

You are the leader of a group of climate scientists concerned about the planet's dwindling rainforests.

My previous post demonstrated how to use transfer learning to build a model that with just 300 training images can classify photos of three different types of Arctic wildlife with 95% accuracy.

My post introducing convolutional neural networks (CNNs) used a dataset with photos of Arctic foxes, polar bears, and walruses to train a CNN to recognize Artic wildlife.

Given a set of images with a relatively high degree of separation between classes, it is perfectly feasible to train a CNN to classify those images on a typical laptop or PC.

Computer vision is a branch of AI in which computers extract perceptual information from images.

It’s not difficult to use Scikit-learn to build machine-learning models that analyze text for sentiment, identify spam e-mails, and classify textual data in other ways.

My previous post described how to build a neural network that serves as a binary classifier. Here’s a binary classifier that accepts two inputs, has a hidden layer with 128 neurons, and outputs a value from 0.0 to 1.0 representing...

One of the common uses for machine learning is performing binary classification, which looks at an input and predicts which of two possible classes it belongs to. Practical uses include sentiment analysis, spam detection, and credit-card fraud detection. Such models...