In-Depth Guide to Recurrent Neural Networks (RNNs) in 2021

Neural networks are powering a wide range of deep learning applications in different industries with different use cases from natural language processing (NLP) to computer vision and drug discovery. In this article, we will explore RNNs and their use cases. Recurrent neural networks (RNNs) are a class of neural networks that takes the output from previous steps as input to the current step. In this sense, RNNs have a "memory" of what has been calculated before. This makes these algorithms fit for sequential problems such as natural language processing (NLP), speech recognition, or time series analysis where current observations depend on previous ones.