Transformers for Machine Learning

A Deep Dive

Uday Kamath, Kenneth Graham, Wael Emara, Kenneth L. Graham

• Publisher: Chapman and Hall/CRC
• Year: 2022
• Print ISBN: 9780367771652
• eBook ISBN: 9781000587098
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Engineering
• Index: Computer Science

Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time Series, and Computer Vision. Transformers have gone through many adaptations and alterations, resulting in newer techniques and methods. Transformers for Machine Learning: A Deep Dive is the first comprehensive book on transformers.

Key Features:

• A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers.
• 60+ transformer architectures covered in a comprehensive manner.
• A book for understanding how to apply the transformer techniques in speech, text, time series, and computer vision.
• Practical tips and tricks for each architecture and how to use it in the real world.
• Hands-on case studies and code snippets for theory and practical real-world analysis using the tools and libraries, all ready to run in Google Colab.

The theoretical explanations of the state-of-the-art transformer architectures will appeal to postgraduate students and researchers (academic and industry) as it will provide a single entry point with deep discussions of a quickly moving field. The practical hands-on case studies and code will appeal to undergraduate students, practitioners, and professionals as it allows for quick experimentation and lowers the barrier to entry into the field.

CHAPTER 1Deep Learning and Transformers: An Introduction

DOI: 10.1201/9781003170082-1

TRANSFORMERS are deep learning models that have achieved state-of-the-art performance in several fields such as natural language processing, computer vision, and speech recognition. Indeed, the massive surge of recently proposed transformer model variants has meant researchers and practitioners alike find it challenging to keep pace. In this chapter, we provide a brief history of diverse research directly or indirectly connected to the innovation of transformers. Next, we discuss a taxonomy based on changes in the architecture for efficiency in computation, memory, applications, etc., which can help navigate the complex innovation space. Finally, we provide resources in tools, libraries, books, and online courses that the readers can benefit from in their pursuit.

1.1 Deep Learning: A Historic Perspective

In the early 1940s, S. McCulloch and W. Pitts, using a simple electrical circuit called a “threshold logic unit”, simulated intelligent behavior by emulating how the brain works [179]. The simple model had the first neuron with inputs and outputs that would generate an output 0 when the “weighted sum” was below a threshold and 1 otherwise, which later became the basis of all the neural architectures. The weights were not learned but adjusted. In his book The Organization of Behaviour (1949), Donald Hebb laid the foundation of complex neural processing by proposing how neural pathways can have multiple neurons firing and strengthening over time [108]. Frank Rosenblatt, in his seminal work, extended the McCulloch–Pitts neuron, referring to it as the “Mark I Perceptron”; given the inputs, it generated outputs using linear thresholding logic [212].

The weights in the perceptron were “learned” by repeatedly passing the inputs and reducing the difference between the predicted output and the desired output, thus giving birth to the basic neural learning algorithm. Marvin Minsky and Seymour Papert later published the book Perceptrons which revealed the limitations of perceptrons in learning the simple exclusive-or function (XOR) and thus prompting the so-called The First AI Winter [186].

John Hopfield introduced “Hopfield Networks”, one of the first recurrent neural networks (RNNs) that serve as a content-addressable memory system [117].

In 1986, David Rumelhart, Geoff Hinton, and Ronald Williams published the seminal work “Learning representations by back-propagating errors” [217]. Their work confirms how a multi-layered neural network using many “hidden” layers can overcome the weakness of perceptrons in learning complex patterns with relatively simple training procedures. The building blocks for this work had been laid down by various research over the years by S. Linnainmaa, P. Werbos, K. Fukushima, D. Parker, and Y. LeCun [91, 149, 164, 196, 267].

LeCun et al., through their research and implementation, led to the first widespread application of neural networks to recognize the hand-written digits used by the U.S. Postal Service [150]. This work is a critical milestone in deep learning history, proving the utility of convolution operations and weight sharing in learning the features in computer vision.

Backpropagation, the key optimization technique, encountered a number of issues such as vanishing gradients, exploding gradients, and the inability to learn long-term information, to name a few [115]. Hochreiter and Schmidhuber, in their work,“Long short-term memory (LSTM)” architecture, demonstrated how issues with long-term dependencies could overcome shortcomings of backpropagation over time [116].

Hinton et al. published a breakthrough paper in 2006 titled “A fast learning algorithm for deep belief nets”; it was one of the reasons for the resurgence of deep learning [113]. The research highlighted the effectiveness of layer-by-layer training using unsupervised methods followed by supervised “fine-tuning” to achieve state-of-the-art results in character recognition. Bengio et al., in their seminal work following this, offered deep insights into why deep learning networks with multiple layers can hierarchically learn features as compared to shallow neural networks [27]. In their research, Bengio and LeCun emphasized the advantages of deep learning through architectures such as convolutional neural networks (CNNs), restricted Boltzmann machines (RBMs), and deep belief networks (DBNs), and through techniques such as unsupervised pre-training with fine-tuning, thus inspiring the next wave of deep learning [28]. Fei-Fei Li, head of the artificial intelligence lab at Stanford University, along with other researchers, launched ImageNet, which resulted in the most extensive collection of images and, for the first time, highlighted the usefulness of data in learning essential tasks such as object ...

Table of contents

• Cover Page
• Half-Title Page
• Series Page
• Title Page
• Copyright Page
• Dedication Page
• Contents
• Foreword
• Preface
• Authors
• Contributors
• Chapter 1 Deep Learning and Transformers: An Introduction
• Chapter 2 Transformers: Basics and Introduction
• Chapter 3 Bidirectional Encoder Representations from Transformers (BERT)
• Chapter 4 Multilingual Transformer Architectures
• Chapter 5 Transformer Modifications
• Chapter 6 Pre-trained and Application-Specific Transformers
• Chapter 7 Interpretability and Explainability Techniques for Transformers
• Bibliography
• Index