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.
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...