Archive | Long Short-Term Memory Networks

Long Short-Term Memory Networks

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

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

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 […]

Continue Reading 4
Line Plots of Air Pollution Time Series

Multivariate Time Series Forecasting with LSTMs in Keras

Neural networks like Long Short-Term Memory (LSTM) recurrent neural networks are able to almost seamlessly model problems with multiple input variables. This is a great benefit in time series forecasting, where classical linear methods can be difficult to adapt to multivariate or multiple input forecasting problems. In this tutorial, you will discover how you can […]

Continue Reading 41
5 Examples of Simple Sequence Prediction Problems for Learning LSTM Recurrent Neural Networks

5 Examples of Simple Sequence Prediction Problems for Learning LSTM Recurrent Neural Networks

Sequence prediction is different from traditional classification and regression problems. It requires that you take the order of observations into account and that you use models like Long Short-Term Memory (LSTM) recurrent neural networks that have memory and that can learn any temporal dependence between observations. It is critical to apply LSTMs to learn how […]

Continue Reading 4
One-to-One Sequence Prediction Model Over Time

Gentle Introduction to Models for Sequence Prediction with Recurrent Neural Networks

Sequence prediction is a problem that involves using historical sequence information to predict the next value or values in the sequence. The sequence may be symbols like letters in a sentence or real values like those in a time series of prices. Sequence prediction may be easiest to understand in the context of time series […]

Continue Reading 10
How to One Hot Encode Sequence Classification Data in Python

How to One Hot Encode Sequence Data in Python

Machine learning algorithms cannot work with categorical data directly. Categorical data must be converted to numbers. This applies when you are working with a sequence classification type problem and plan on using deep learning methods such as Long Short-Term Memory recurrent neural networks. In this tutorial, you will discover how to convert your input or […]

Continue Reading 10
How to Remove Trends and Seasonality with a Difference Transform in Python

How to Remove Trends and Seasonality with a Difference Transform in Python

Time series datasets may contain trends and seasonality, which may need to be removed prior to modeling. Trends can result in a varying mean over time, whereas seasonality can result in a changing variance over time, both which define a time series as being non-stationary. Stationary datasets are those that have a stable mean and […]

Continue Reading 2