Machine Learning

Mini-Course on Long Short-Term Memory Recurrent Neural Networks with Keras

Last Updated on August 14, 2019

Long Short-Term Memory (LSTM) recurrent neural networks are one of the most interesting types of deep learning at the moment.

They have been used to demonstrate world-class results in complex problem domains such as language translation, automatic image captioning, and text generation.

LSTMs are different to multilayer Perceptrons and convolutional neural networks in that they are designed specifically for sequence prediction problems.

In this mini-course, you will discover how you can quickly bring LSTM models to your own sequence forecasting problems.

After completing this mini-course, you will know:

  • What LSTMs are, how they are trained, and how to prepare data for training LSTM models.
  • How to develop a suite of LSTM models including stacked, bidirectional, and encoder-decoder models.
  • How you can get the most out of your models with hyperparameter optimization, updating, and finalizing models.

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.

Note: This is a big guide; you may want to bookmark it.

Mini-Course on Long Short-Term Memory Recurrent <a href=To finish reading, please visit source site