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.
Looking to improve your search relevance? Here are five types of search evaluation services that can help you strengthen your search engine.
Working with crowdsourced data vendors unlocks access to an inexpensive, scalable workforce. In this post, we describe key benefits of crowdsourcing data.
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.
To train NLP algorithms, large annotated text datasets are required. Learn more with a brief introduction to five common types of text annotation.
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.
Financial services firms are increasingly implementing AI to improve their CX. Learn how they’re using the technology to tackle the latest industry challenges.
Are you evaluating your annotation team for quality? Read why self-agreement checks are critical for a proper evaluation.
Discover three critical ways human-annotated data improves map and navigation software.
Looking for an introduction to audio classification? In this article we discuss different types of audio classification and their use in machine learning projects.
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