To give you an idea of how useful RNNs and LSTMs are let’s take a sneak peek. The world’s leading tech companies are all using RNNs and LSTMs in their applications. Let’s take a look at some of those. Speech recognition, where a sequence of data samples extracted from an audio signal is continuously mapped to text. Good examples are Google Assistant, Apple’s Siri, Amazon’s Alexa, and Nuance’s Dragon solutions. All of these use RNNs as a part of their speech recognition software. Time series predictions, where we predict traffic patterns on specific roads to help drivers optimize their driving paths, like they do in Waze, or predicting what movie a consumer will want to watch next, like they do in Netflix. Predicting stock price movements based on historical patterns of stock movements and potentially other market conditions, that change over time. This is practiced by most quantitive hedge funds. Natural Language Processing or NLP in short, such as machine translation used by Google or Salesforce for example. Question answering like Google Analytics, if you’ve got a question about your app, you’ll soon be able to ask Google Analytics directly. Have you heard of chatbots? Many companies such as Google, Baidu, and, Slack are using RNNs to drive their Natural Language Processing engines for such applications. And of course, there are many other NLP applications. The last application we will focus on is gesture recognition, where we observe a sequence of video frames to determine which gesture the user has made. For example, waiving your right arm or swinging it. Here are a few gesture recognition technology companies and companies that are applying gesture recognition to their products.