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 […]
In part 1, a gentle introduction to positional encoding in transformer models, we discussed the positional encoding layer of the transformer model. We also showed how you could implement this layer and its functions yourself in Python. In this tutorial, you’ll implement the positional encoding layer in Keras and Tensorflow. You can then use this […]
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, recurrent neural networks have an inbuilt mechanism that deals with the order of sequences. The transformer model, however, does not use recurrence or […]
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 to 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 implemented by RNN-based encoder-decoder architectures. The Transformer model revolutionized the implementation of attention by dispensing with recurrence and convolutions and, alternatively, relying solely on a self-attention mechanism. We will first focus on the Transformer attention mechanism in this tutorial […]
This tutorial is designed for anyone looking for an understanding of how recurrent neural networks (RNN) work and how to use them via the Keras deep learning library. While the Keras library provides all the methods required for solving problems and building applications, it is also important to gain an insight into how everything works. […]
When it comes to sequential or time series data, traditional feedforward networks cannot be used for learning and prediction. A mechanism is required to retain past or historical information to forecast future values. Recurrent neural networks, or RNNs for short, are a variant of the conventional feedforward artificial neural networks that can deal with sequential […]
The Luong attention sought to introduce several improvements over the Bahdanau model for neural machine translation, notably 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, regardless 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 […]
Deep learning networks have gained immense popularity in the past few years. The “attention mechanism” is integrated with deep learning networks to improve their performance. Adding an attention component to the network has shown significant improvement in tasks such as machine translation, image recognition, text summarization, and similar applications. This tutorial shows how to add […]
