Machine Learning

Demonstration of Memory with a Long Short-Term Memory Network in Python

Last Updated on August 27, 2020

Long Short-Term Memory (LSTM) networks are a type of recurrent neural network capable of learning over long sequences.

This differentiates them from regular multilayer neural networks that do not have memory and can only learn a mapping between input and output patterns.

It is important to understand the capabilities of complex neural networks like LSTMs on small contrived problems as this understanding will help you scale the network up to large and even very large problems.

In this tutorial, you will discover the capability of LSTMs to remember and recall.

After completing this tutorial, you will know:

  • How to define a small sequence prediction problem that only an RNN like LSTMs can solve using memory.
  • How to transform the problem representation so that it is suitable for learning by LSTMs.
  • How to design an LSTM to solve the problem correctly.

Kick-start your project with my new book Long Short-Term Memory Networks With Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

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