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 data and can be trained to hold knowledge about the past. After completing this tutorial, you will know: Recurrent neural networks What is meant by […]

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. In this article, the computations taking place in the RNN model are shown step by step. Next, a complete end-to-end system for time series prediction […]

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 and subsequently review the Transformer model in a separate one.  In this tutorial, you will discover the Transformer attention mechanism for neural machine translation.  After […]

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, you will discover the network architecture of the Transformer model. After completing this tutorial, you will know: How the Transformer architecture implements an encoder-decoder structure […]

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 convolution and treats each data point as independent of the other. Hence, positional information is added to the model explicitly to retain the information regarding […]

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 layer in a complete transformer model. After completing this tutorial, you will know: Text vectorization in Keras Embedding layer in Keras How to subclass the […]

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 the Transformer encoder and decoder. Our end goal will be to apply the complete Transformer model to Natural Language Processing (NLP). In this tutorial, you […]

We have already familiarized ourselves with the theory behind the Transformer model and its attention mechanism. We have already started our journey of implementing a complete model by seeing how to implement the scaled-dot product attention. We shall now progress one step further into our journey by encapsulating the scaled-dot product attention into a multi-head attention mechanism, which is a core component. Our end goal remains to apply the complete model to Natural Language Processing (NLP). In this tutorial, you […]

With the Transformer architecture revolutionizing the implementation of attention, and achieving very promising results in the natural language processing domain, it was only a matter of time before we could see its application in the computer vision domain too. This was eventually achieved with the implementation of the Vision Transformer (ViT).  In this tutorial, you will discover the architecture of the Vision Transformer model, and its application to the task of image classification. After completing this tutorial, you will know: […]

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 to implement the Transformer encoder from scratch in TensorFlow and Keras.  After completing this tutorial, you will know: The layers that form part of the […]