Search results for "attention"

Gentle Introduction to Global Attention for Encoder-Decoder Recurrent Neural Networks

Gentle Introduction to Global Attention for Encoder-Decoder Recurrent Neural Networks

The encoder-decoder model provides a pattern for using recurrent neural networks to address challenging sequence-to-sequence prediction problems such as machine translation. Attention is an extension to the encoder-decoder model that improves the performance of the approach on longer sequences. Global attention is a simplification of attention that may be easier to implement in declarative deep […]

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Implementation Patterns for the Encoder-Decoder RNN Architecture with Attention

Implementation Patterns for the Encoder-Decoder RNN Architecture with Attention

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. Attention is a mechanism that addresses a limitation of the encoder-decoder architecture on long sequences, and that in general speeds up the learning and lifts the skill of the […]

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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 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 […]

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Feeding Hidden State as Input to Decoder

How Does Attention Work in Encoder-Decoder Recurrent Neural Networks

Attention is a mechanism that was developed to improve the performance of the Encoder-Decoder RNN on machine translation. In this tutorial, you will discover the attention mechanism for the Encoder-Decoder model. After completing this tutorial, you will know: About the Encoder-Decoder model and attention mechanism for machine translation. How to implement the attention mechanism step-by-step. […]

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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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Why Use Ensemble Learning

Why Use Ensemble Learning?

What are the Benefits of Ensemble Methods for Machine Learning? Ensembles are predictive models that combine predictions from two or more other models. Ensemble learning methods are popular and the go-to technique when the best performance on a predictive modeling project is the most important outcome. Nevertheless, they are not always the most appropriate technique […]

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Dimensionality Reduction Algorithms With Python

6 Dimensionality Reduction Algorithms With Python

Dimensionality reduction is an unsupervised learning technique. Nevertheless, it can be used as a data transform pre-processing step for machine learning algorithms on classification and regression predictive modeling datasets with supervised learning algorithms. There are many dimensionality reduction algorithms to choose from and no single best algorithm for all cases. Instead, it is a good […]

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Histogram Plots of Robust Scaler Transformed Input Variables for the Sonar Dataset

How to Scale Data With Outliers for Machine Learning

Many machine learning algorithms perform better when numerical input variables are scaled to a standard range. This includes algorithms that use a weighted sum of the input, like linear regression, and algorithms that use distance measures, like k-nearest neighbors. Standardizing is a popular scaling technique that subtracts the mean from values and divides by the […]

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A Gentle Introduction to Dimensionality Reduction for Machine Learning

Introduction to Dimensionality Reduction for Machine Learning

The number of input variables or features for a dataset is referred to as its dimensionality. Dimensionality reduction refers to techniques that reduce the number of input variables in a dataset. More input features often make a predictive modeling task more challenging to model, more generally referred to as the curse of dimensionality. High-dimensionality statistics […]

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