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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
Backpropagation Through Time, or BPTT, is the training algorithm used to update weights in recurrent neural networks like LSTMs. To effectively frame sequence prediction problems for recurrent neural networks, you must have a strong conceptual understanding of what Backpropagation Through Time is doing and how configurable variations like Truncated Backpropagation Through Time will affect the […]
Deep learning libraries assume a vectorized representation of your data. In the case of variable length sequence prediction problems, this requires that your data be transformed such that each sequence has the same length. This vectorization allows code to efficiently perform the matrix operations in batch for your chosen deep learning algorithms. In this tutorial, […]
Bidirectional LSTMs are an extension of traditional LSTMs that can improve model performance on sequence classification problems. In problems where all timesteps of the input sequence are available, Bidirectional LSTMs train two instead of one LSTMs on the input sequence. The first on the input sequence as-is and the second on a reversed copy of […]
A powerful feature of Long Short-Term Memory (LSTM) recurrent neural networks is that they can remember observations over long sequence intervals. This can be demonstrated by contriving a simple sequence echo problem where the entire input sequence or partial contiguous blocks of the input sequence are echoed as an output sequence. Developing LSTM recurrent neural […]
