In languages the order of the words and their position in a sentence really matters. The meaning of the entire sentence can change if the words are re-ordered. When implementing NLP solutions, the recurrent neural networks have an inbuilt mechanism that deals with the order of sequences. The transformer model, however, does not use recurrence […]
We have already familiarized ourselves with the concept of self-attention as implemented by the Transformer attention mechanism for neural machine translation. We will now be shifting our focus on the details of the Transformer architecture itself, to discover how self-attention can be implemented without relying on the use of recurrence and convolutions. In this tutorial, […]
Before the introduction of the Transformer model, the use of attention for neural machine translation was being implemented by RNN-based encoder-decoder architectures. The Transformer model revolutionized the implementation of attention by dispensing of recurrence and convolutions and, alternatively, relying solely on a self-attention mechanism. We will first be focusing on the Transformer attention mechanism in […]
The Luong attention sought to introduce several improvements over the Bahdanau model for neural machine translation, particularly by introducing two new classes of attentional mechanisms: a global approach that attends to all source words, and a local approach that only attends to a selected subset of words in predicting the target sentence. In this tutorial, […]
Conventional encoder-decoder architectures for machine translation encoded every source sentence into a fixed-length vector, irrespective of its length, from which the decoder would then generate a translation. This made it difficult for the neural network to cope with long sentences, essentially resulting in a performance bottleneck. The Bahdanau attention was proposed to address the performance […]
As the popularity of attention in machine learning grows, so does the list of neural architectures that incorporate an attention mechanism. In this tutorial, you will discover the salient neural architectures that have been used in conjunction with attention. After completing this tutorial, you will gain a better understanding of how the attention mechanism is […]
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 of the encoded input vectors, with the most […]
Attention is becoming increasingly popular in machine learning, but what makes it such an attractive concept? What is the relationship between attention as applied in artificial neural networks, and its biological counterpart? What are the components that one would expect to form an attention-based system in machine learning? In this tutorial, you will discover an […]
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, there is one core quality they can all agree on: attention is a mechanism for making both biological and artificial neural systems more flexible. In […]
