This is a tutorial on building a deep learning model to predict stock prices using the long short-term memory (LSTM) architecture and the TensorFlow library. Stock price prediction is a challenging and controversial problem, as there are many factors that can influence the price of a stock. Some of these factors include economic indicators, company performance, and market trends. In this tutorial, I will demonstrate how to use LSTM layers and the TensorFlow library to build a model that can predict stock prices with a high degree of accuracy. Read Full Article Here
LSTM is a type of recurrent neural network (RNN) that is particularly well-suited for time series data, such as stock prices. LSTM networks are able to remember past information and use it to make predictions about future events. This makes them particularly powerful for predicting stock prices, as stock prices are often influenced by past events.
In this tutorial, I will walk you through the steps of building and training an LSTM model to predict stock prices. I will start by discussing the data and how to preprocess it. I will then demonstrate how to build and train the LSTM model using TensorFlow. Finally, the trained model will be used to make predictions on new stock price data. By the end of this tutorial, you will have a working LSTM model that you can use to predict stock prices on your own.
