Join us for a 1-hour deep dive and learn how to use ChatGPT and the ChatGPT API to write smarter apps and improve your business practices.
People the world over are using ChatGPT to transform the way they do business. Sales executives use it to create scoping documents from transcripts of conversations with clients. Marketing directors use it to generate ad copy, while programmers use it...
Unless you’ve lived in a cave for the last few months, you’ve heard of ChatGPT. It’s a deep-learning model (neural network) created by OpenAI whose ability to generate human-like prose has made AI the topic of dinner-table conversations around the...
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.