Tag Archives | natural language processing

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Inferencing the Transformer Model

We have seen how to train the Transformer model on a dataset of English and German sentence pairs and how to plot the training and validation loss curves to diagnose the model’s learning performance and decide at which epoch to run inference on the trained model. We are now ready to run inference on the […]

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Training the Transformer Model

We have put together the complete Transformer model, and now we are ready to train it for neural machine translation. We shall use a training dataset for this purpose, which contains short English and German sentence pairs. We will also revisit the role of masking in computing the accuracy and loss metrics during the training […]

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Implementing the Transformer Decoder from Scratch in TensorFlow and Keras

There are many similarities between the Transformer encoder and decoder, such as their implementation of multi-head attention, layer normalization, and a fully connected feed-forward network as their final sub-layer. Having implemented the Transformer encoder, we will now go ahead and apply our knowledge in implementing the Transformer decoder as a further step toward implementing the […]

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Implementing the Transformer Encoder from Scratch in TensorFlow and Keras

Having seen how to implement the scaled dot-product attention and integrate it within the multi-head attention of the Transformer model, let’s progress one step further toward implementing a complete Transformer model by applying its encoder. Our end goal remains to apply the complete model to Natural Language Processing (NLP). In this tutorial, you will discover how […]

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How to Implement Scaled Dot-Product Attention from Scratch in TensorFlow and Keras

Having familiarized ourselves with the theory behind the Transformer model and its attention mechanism, we’ll start our journey of implementing a complete Transformer model by first seeing how to implement the scaled-dot product attention. The scaled dot-product attention is an integral part of the multi-head attention, which, in turn, is an important component of both […]

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The Attention Mechanism from Scratch

The attention mechanism was introduced to improve the performance of the encoder-decoder model for machine translation. The idea behind the attention mechanism was to permit the decoder to utilize the most relevant parts of the input sequence in a flexible manner, by a weighted combination of all the encoded input vectors, with the most relevant […]

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A Bird’s Eye View of Research on Attention

Attention is a concept that is scientifically studied across multiple disciplines, including psychology, neuroscience, and, more recently, machine learning. While all disciplines may have produced their own definitions for attention, one core quality they can all agree on is that attention is a mechanism for making both biological and artificial neural systems more flexible.  In […]

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