There are subtle differences between AI and its related fields: machine learning and deep learning. Let’s take a closer look at these terms.
Discover the difference between CNN and RNN and how they are used in computer vision and natural language processing.
It’s only logical to ask how much training data you need, but it can be a complicated question to answer. Let’s take a look at why.
Sentiment analysis involves classifying the subjective, contextual information within text data. Read our beginner’s guide to learn more.
Discover the most common types of image annotation for computer vision AI to help you pick the right tools and resources for your projects.
Trust is fundamental to any use of machine learning — healthcare is no exception. Read on as we look at trust and machine learning in medicine.
Although many people use the terms text mining and text analytics interchangeably, there are key differences. Learn what text mining is, the processessing techniques used and its practical applications.
Discover three critical ways human-annotated data improves map and navigation software.
Working with crowdsourced data vendors unlocks access to an inexpensive, scalable workforce. In this post, we describe key benefits of crowdsourcing data.
To train NLP algorithms, large annotated text datasets are required. Learn more with a brief introduction to five common types of text annotation.
Capturing enough accurate, quality data at scale is a common challenge. Discover four ways to source raw data for machine learning, and how to go about annotation process.
Facial recognition is a field within AI and computer science that seeks to give machines the ability to interpret human faces. This beginner’s guide explains types of face recognition processes, how they work, various applications and how accurate they are today.
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