10 of the Most Important Recurrent Neural Networks For AI

Author(s): Anil Tilbe

Originally published on Towards AI.

Advantages of recurrent neural networks along with top 10 networks, top use cases, challenges, and best practices

This member-only story is on us. Upgrade to access all of Medium.

By Pixabay from Pexels

Recurrent neural networks (RNN) are a type of artificial intelligence that is used to model data that exhibit temporal or sequential behavior [1]. This type of neural network is well suited for handwriting recognition and machine translation tasks. RNNs can learn complex patterns in data and store information for long periods.

Traditional neural networks are not well suited for [25] modeling data that exhibit temporal or sequential behavior because traditional neural networks treat each input vector as an independent observation [2]. RNNs are designed to model data… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI