Deep learning is vast field that employs artificial neural networks to process data and train a machine learning model. Within deep learning, two learning approaches are used, supervised and unsupervised. This tutorial focuses on recurrent neural networks (RNN), which use supervised deep learning and sequential learning to develop a model. This deep learning technique is especially useful when handling time series data, as is used in this tutorial.
When creating any machine learning model, it’s important to understand the data that you’re analyzing so that you can use the most relevant model architecture. In this tutorial, the goal is to create a basic model that can predict a stock’s value using daily Open, High, Low, and Close values. Because the stock market can be extremely volatile, there are many factors that can influence and contribute to a stock’s value. This tutorial uses the following parameters for the stock data.
Open: The stock’s
Original URL: https://developer.ibm.com/tutorials/build-a-recurrent-neural-network-pytorch/