By David Petersson, TechTarget
March 24, 2021: Convolutional neural networks and recurrent neural nets underlie many of the AI applications that drive business value. Learn about CNNs vs. RNNs in this primer.
To set realistic expectations of AI — without missing opportunities — it is important to understand algorithms, both their capabilities and limitations.
In this article, we explore two algorithms that have propelled the field of AI forward — convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We will cover what they are, how they work, what their limitations are and where they complement each other.
But first, a brief summary of the main differences between a CNN vs. an RNN.
Here’s Kore.ai CTO Prasanna Arikala’s take on the subject:
“CNNs are preferred in interpreting visual data, sparse data or data that does not come in sequence. Recurrent neural networks, on the other hand, are designed to recognize sequential or temporal data. They make better predictions considering the order or sequence of the data as they relate to previous or the next data nodes.”
