Archive | Long Short-Term Memory Networks

Long Short-Term Memory Networks

How to Develop an Encoder-Decoder Model with Attention for Sequence-to-Sequence Prediction in Keras

How to Develop an Encoder-Decoder Model with Attention for Sequence-to-Sequence Prediction in Keras

The encoder-decoder architecture for recurrent neural networks is proving to be powerful on a host of sequence-to-sequence prediction problems in the field of natural language processing such as machine translation and caption generation. Attention is a mechanism that addresses a limitation of the encoder-decoder architecture on long sequences, and that in general speeds up the […]

Continue Reading 2
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 […]

Continue Reading 6
Example of LSTMs used in Automatic Handwriting Generation

Gentle Introduction to Generative Long Short-Term Memory Networks

The Long Short-Term Memory recurrent neural network was developed for sequence prediction. In addition to sequence prediction problems. LSTMs can also be used as a generative model In this post, you will discover how LSTMs can be used as generative models. After completing this post, you will know: About generative models, with a focus on […]

Continue Reading 0
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 […]

Continue Reading 6
Convolutional Neural Network Long Short-Term Memory Networks

CNN Long Short-Term Memory Networks

Gentle introduction to CNN LSTM recurrent neural networks with example Python code. Input with spatial structure, like images, cannot be modeled easily with the standard Vanilla LSTM. The CNN Long Short-Term Memory Network or CNN LSTM for short is an LSTM architecture specifically designed for sequence prediction problems with spatial inputs, like images or videos. […]

Continue Reading 24