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 […]

Understanding ChatGPT

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 […]

Object Detection

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.

Face Detection

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.

Data Augmentation

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.

Transfer Learning

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.

Pretrained CNNs

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.