How to Find Training Data for Machine Learning
Supervised machine learning projects require training data. By learning from training data, a supervised algorithm aims to be able to accurately predict outcomes when exposed to real data. Training data is required for all types of supervised machine learning projects:...
How Drones and Computer Vision are Used to Enhance Crop Yield
Drones or UAVs (Unmanned Aerial Vehicles) are propelling a revolution in farming and agriculture. In 2019, the FAO published E-Agriculture in Action: Drones for Agriculture, a detailed guide and roadmap of how drones are being used in agriculture, and the best practices for...
Ultimate Guide to LIDAR
Light Detection and Ranging (LIDAR) is a remote sensing and mapping technology designed to measure the dimensions and topography of terrain and 3D spaces. While LIDAR has been in development since the 1960s, recent developments in laser sensing and data...
Digital Cryptids: How AI is Making Monsters
AI-generated art has become a hot topic. We now live in a world where AI art engines like DALL-E 2, MidJourney and Stable Diffusion create spectacular artworks that seem to possess the artistic skill, expansive imagination and visionary creativity we associate...
Speech Recognition: Opportunities and Challenges
Audio and text transcription has long been a cornerstone of machine learning. There are two core functions of audio and speech transcription, which partly sit within the natural language processing (NLP) discipline of artificial intelligence. The first function is turning speech...
Semantic Segmentation for Computer Vision Projects Explained
Semantic segmentation is an image-labeling technique to create data for supervised computer vision (CV) models. In its simplest terms, the objective is to assign a class label to each pixel in an image. For example, if you’re labeling a cat...
News, Research, and Social Media as Sources for Datasets
In the age of big data, datasets are crucial for research, analysis, and decision-making in various industries. But where do these datasets come from? Traditional sources, such as government agencies and academic institutions, are still important, but there is a...
Basic Data Collection Methods for Machine Learning Projects Explained
Data collection might seem like a simple process on the surface. But because it’s the basic building block of all ML projects, the data needs to be accurate, relevant, and cover all iterations of a problem. Consequently, it is crucial...
The Challenges of Text, Audio, Photo, and Video Data Collection for ML Training Models
The basis of all machine learning projects is data collection and acquisition. But that is also the first stumbling block where many ML projects fail. In this article, we will discuss the challenges of text, audio, photo, and video data...
What Is AI Training Data? – And Why It Is the Basis of All AI Projects
Artificial Intelligence is a transformative technology that has found its way into various aspects of our lives, from voice assistants on our smartphones to autonomous vehicles navigating our streets. But have you ever wondered how AI systems learn and improve...