Last Updated on August 14, 2019 Long Short-Term Memory or LSTM recurrent neural networks are capable of learning and remembering over long sequences of inputs. LSTMs work very well if your problem has one output for every input, like time series forecasting or text translation. But LSTMs can be challenging to use when you have very long input sequences and only one or a handful of outputs. This is often called sequence labeling, or sequence classification. Some examples include: Classification […]

Last Updated on August 14, 2019 Recurrent neural networks are able to learn the temporal dependence across multiple timesteps in sequence prediction problems. Modern recurrent neural networks like the Long Short-Term Memory, or LSTM, network are trained with a variation of the Backpropagation algorithm called Backpropagation Through Time. This algorithm has been modified further for efficiency on sequence prediction problems with very long sequences and is called Truncated Backpropagation Through Time. An important configuration parameter when training recurrent neural networks […]

Last Updated on August 14, 2019 The Encoder-Decoder architecture is popular because it has demonstrated state-of-the-art results across a range of domains. A limitation of the architecture is that it encodes the input sequence to a fixed length internal representation. This imposes limits on the length of input sequences that can be reasonably learned and results in worse performance for very long input sequences. In this post, you will discover the attention mechanism for recurrent neural networks that seeks to […]

Last Updated on August 14, 2019 Recurrent neural networks, or RNNs, are a type of artificial neural network that add additional weights to the network to create cycles in the network graph in an effort to maintain an internal state. The promise of adding state to neural networks is that they will be able to explicitly learn and exploit context in sequence prediction problems, such as problems with an order or temporal component. In this post, you are going take […]

Last Updated on August 14, 2019 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 output sequence data to a one hot encoding for use in sequence classification problems with deep learning in Python. […]

Last Updated on August 25, 2019 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 forecasting as the problem is already generally understood. In this post, you will discover the standard sequence prediction models […]