Last Updated on August 27, 2020 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 the input sequence. This can provide additional context to the network and result in faster and even fuller learning […]

Last Updated on August 14, 2019 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, you will discover techniques that you can use to prepare your variable length sequence data for sequence prediction problems […]

Last Updated on August 14, 2020 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 skill, stability, and speed when training your network.In this post, you will get a gentle introduction to Backpropagation Through […]

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 […]