Subject archive for "deep-learning"

Deep learning is a type of machine learning and artificial intelligence (AI) that imitates how humans learn by example. While that sounds complex, the basic idea behind deep learning is simple. Deep learning models are taught to classify data from images (such as “cat vs. dog”), sound (“meow vs. bark”), or text (“tabby vs. schnauzer”). These models build a hierarchy where each layer is based on knowledge gained from the preceding layer, and iterations continue until its accuracy goal is reached. Deep learning models often achieve accuracy that rivals what humans can determine, in a fraction of the time.

Go to your favourite social media outlet and use the search functionality to look for DALL-E. You can take a look at this link to see some examples in Twitter. Scroll a bit up and down, and you will see some images that, at first sight, may be very recognisable. Depending on the scenes depicted, if you pay a bit more attention you may see that in some cases something is not quite right with the images. At best there may be a bit (or a lot) of distortion, and in some other cases the scene is totally wacky. No, the artist did not intend to include that distortion or wackiness, and for that matter it is quite likely the artist is not even human. After all, DALL-E is a computer model, called so as a portmanteau of the beloved Pixar robot Wall-E and the surrealist artist Salvador Dalí.

This article provides a brief introduction to natural language using spaCy and related libraries in Python.

In the last few years, machine learning and deep learning have become a core part of fields like healthcare, retail, banking, and insurance. This list can go on indefinitely—there's almost no field where machine learning (ML) is not applied to improve the overall efficiency and accuracy of systems. As the world's economy has rapidly grown, so has the need to do things automatically.

This article provides an excerpt "Deep Reinforcement Learning" from the book, Deep Learning Illustrated by Krohn, Beyleveld, and Bassens. The article includes an overview of reinforcement learning theory with focus on the deep Q-learning. It also covers using Keras to construct a deep Q-learning network that learns within a simulated video game environment. Many thanks to Addison-Wesley Professional for the permission to excerpt the chapter.

In this article we talk about the foundations of Computer vision, the history and capabilities of the OpenCV framework, and how to make your first steps in accessing and visualising images with Python and OpenCV.