Class imbalance is a common challenge in real-world machine learning applications. It occurs when the classes within a dataset are not represented equally. For instance, in credit card fraud detection, normal transactions might comprise 99.9% of the dataset, while fraudulent transactions make up …

Deploying deep learning models for real-time visual tasks like fruit ripeness classification in smart agriculture is challenging due to edge hardware constraints. This research presents an optimized lightweight object detection model for edge deployment, demonstrated through a tomato ripeness …

The global financial ecosystem is a deeply interconnected web where entities, ranging from multinational corporations to key executives, are linked in complex ways. Shaking one of entity often sends immediate and no-obvious ripples down this web. Due to the sheer size of the financial world, these …

In this post, we’ll learn how to add a “steering wheel” to the GAN, and we will be turning our basic GAN into a Conditional GAN (CGAN) that will allow us to control the faces it generates.

In this post, we’ll walk through the process of building and training a GAN from the ground up to generate photorealistic images of human faces.

Learn to deploy AI on edge devices! We build a baseline YOLOv11n model for visual classification and benchmark its performance, setting the stage for crucial optimization for Raspberry Pi, Jetson, and more.

Learn to deploy AI on edge devices! We build a baseline YOLOv11n model for visual classification and benchmark its performance, setting the stage for crucial optimization for Raspberry Pi, Jetson, and more.

Unlock the power of custom layers in PyTorch! This guide provides software engineers with a deep dive into creating and utilizing custom layers for AI applications, including practical examples and advanced optimization techniques. Learn to extend PyTorch and tailor models for specific tasks.

This post explores the process of creating a text classification model using PyTorch and the AG News dataset. We’ll cover every stage from dataset loading to model evaluation, providing a comprehensive understanding of text classification and deep learning concepts.

We’ll walk through building and comparing three different neural network architectures for classifying fashion items using the Fashion MNIST dataset.