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 […]
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 better understand how the attention mechanism is incorporated into different […]
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 […]
Attention is becoming increasingly popular in machine learning, but what makes it such an attractive concept? What is the relationship between attention applied in artificial neural networks and its biological counterpart? What components would one expect to form an attention-based system in machine learning? In this tutorial, you will discover an overview of attention and […]
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 […]
When you work on a machine learning problem related to images, not only do you need to collect some images as training data, but you also need to employ augmentation to create variations in the image. It is especially true for more complex object recognition problems. There are many ways for image augmentation. You may […]
Machine learning is a field of study and is concerned with algorithms that learn from examples. Classification is a task that requires the use of machine learning algorithms that learn how to assign a class label to examples from the problem domain. An easy to understand example is classifying emails as “spam” or “not spam.” […]
An imbalanced classification problem is a problem that involves predicting a class label where the distribution of class labels in the training dataset is skewed. Many real-world classification problems have an imbalanced class distribution, therefore it is important for machine learning practitioners to get familiar with working with these types of problems. In this tutorial, […]
Generative Adversarial Networks, or GANs for short, were first described in the 2014 paper by Ian Goodfellow, et al. titled “Generative Adversarial Networks.” Since then, GANs have seen a lot of attention given that they are perhaps one of the most effective techniques for generating large, high-quality synthetic images. As such, a number of books […]
