Computer vision object counting
Object counting is a crucial task in computer vision that involves determining the number of objects in an image or video sequence. In this paper, we explore the different approaches and applications of object counting in computer vision.
Revolutionizing Transportation: The Future of Self-Driving Cars with Computer Vision
Computer vision is a critical component of self-driving cars, a hot topic in recent years. We examine this topic in this article.
Smart farming and artificial intelligence
The fourth agricultural revolution is already under way with the adoption of smart farm technology such as artificial intelligence, machine learning, robotics, drones, and 5G. This technology is expected to make farming more efficient and productive while reducing environmental impact.
A Brief Conversation with ChatGPT About Computer Vision and AI
This article aims to shed light on the field of computer vision and artificial intelligence through a series of questions addressed to ChatGPT.
Applications of Computer Vision in waste management
This article discusses the integration of artificial intelligence in the field of recycling. We will also train a model on the AIEX platform that can effectively sort waste materials using computer vision technology.
Ensemble Machine Learning
Ensemble machine learning is a powerful technique that leverages the strengths of multiple weak learning models, also known as base models, to improve the accuracy and stability of predictions. Ensemble methods can produce more robust and reliable predictions than any single model alone. This approach is particularly useful in complex problems where a single model may struggle to capture all the nuances and variations of the data.
Various Types of Regularization
Regularization is a technique used in Machine Learning and Deep Learning models to prevent overfitting. This paper introduces L1, L2, and dropout regularization methods.
Machine Learning Engineers Should Use Docker
Docker is a platform that enables developers to easily create, deploy, and run applications in containers, and has gained immense popularity since its public release in 2013. In this article, we will provide a brief introduction to Docker, and explore its application in the field of machine learning, where it can be used to create reproducible and portable environments for training and deploying models, ensuring consistency and ease of deployment.
Dataset Development Lifecycle
Google has come up with a framework for data collection inspired by software development concepts in a 5-step cyclical process. In this article, we will examine google’s proposed data collection framework.
How Tensorboard Works
In this article, we will introduce Tensorboard and explain how it can be used on AIEX.
