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Gentle Introduction to Making Predictions with Sequences

Making Predictions with Sequences

Sequence prediction is different from other types of supervised learning problems. The sequence imposes an order on the observations that must be preserved when training models and making predictions. Generally, prediction problems that involve sequence data are referred to as sequence prediction problems, although there are a suite of problems that differ based on the […]

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Encoder-Decoder Long Short-Term Memory Networks

Encoder-Decoder Long Short-Term Memory Networks

Gentle introduction to the Encoder-Decoder LSTMs for sequence-to-sequence prediction with example Python code. The Encoder-Decoder LSTM is a recurrent neural network designed to address sequence-to-sequence problems, sometimes called seq2seq. Sequence-to-sequence prediction problems are challenging because the number of items in the input and output sequences can vary. For example, text translation and learning to execute […]

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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 […]

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LSTM-400

Long Short-Term Memory Networks With Python

Long Short-Term Memory Networks With Python Develop Deep Learning Models for your Sequence Prediction Problems Sequence Prediction is…important, overlooked, and HARD Sequence prediction is different to other types of supervised learning problems. The sequence imposes an order on the observations that must be preserved when training models and making predictions. There are 4 main types of […]

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Attentional Interpretation of Words in the Input Document to the Output Summary

Attention in Long Short-Term Memory Recurrent Neural Networks

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 […]

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