Predict stock prices with LSTM in PyTorch
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Dive into predicting stock prices using Long Short-Term Memory (LSTM) networks in PyTorch. Learn how to build and train LSTM models to analyze time series data and predict future stock market trends with deep learning.
At a Glance
Learn to predict time series data with Long Short-Term Memory (LSTM) in PyTorch. Create a deep learning model that can predict a stock’s value using daily Open, High, Low, and Close values and practice visualizing results and evaluating your model. Build foundational skills in machine learning while exploring the LSTM architecture. Develop practical knowledge with this beginner-friendly tutorial and apply it to real-world datasets using PyTorch.
This hands-on project is based on the Build a recurrent neural network using Pytorch tutorial. The guided project format combines the instructions of the tutorial with the environment to execute these instructions without the need to download, install, and configure tools.
A look at the project ahead
- Build an LSTM using PyTorch.
- Train an LSTM model and evaluate the model with metrics such as mean squared error.
What you’ll need
- Basic to intermediate knowledge of Python: Familiarity with Python’s core programming concepts and the ability to write and understand Python code.
- An understanding of basic machine learning concepts: Although detailed explanations are provided, some prior knowledge of machine learning principles is beneficial.
- An environment that supports Python: Everything is available in the JupyterLab, including any Python libraries and data sets.
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