Author Profile

Steven Farrell is Professor of Statistics and Computer Science at UC Berkeley and Principal Scientist at Google DeepMind. At Berkeley, he is the Machine Learning Research Director at the Simons Institute for the Theory of Computing, Director of the Foundations of Data Science Institute, and Director of the Collaboration on the Theoretical Foundations of Deep Learning, and he has served as Associate Director of the Simons Institute. He is President of the Association for Computational Learning, Honorary Professor of Mathematical Sciences at the Australian National University. He was awarded the Malcolm McIntosh Prize for Physical Scientist of the Year in Australia in 2001, was chosen as an Institute of Mathematical Statistics Medallion Lecturer in 2008, an IMS Fellow and Australian Laureate Fellow in 2011, a Fellow of the ACM in 2018, and recipient of the Chancellor's Distinguished Service Award in 2023. He was elected to the Australian Academy of Science in 2015. Steve is a Machine Learning Engineer in the Data and Analytics Services group at NERSC. He supports machine learning and deep learning workflows on the NERSC supercomputers and collaborates with scientists for applied ML research.

About The Book

Machine learning and deep learning have rapidly become some of the most transformative technologies of the modern era. These technologies have revolutionized the way we process, analyze, and interpret data. Machine learning and deep learning have found a broad range of applications in industries such as healthcare, finance, transportation, and many others. Neural networks are the foundation of machine learning and deep learning. They are a set of algorithms that are designed to recognize patterns in data. These patterns can be used to make predictions about future data. Recent advances in neural networks include the development of deep neural networks, which are able to learn multiple levels of abstraction from data. Deep neural networks are composed of multiple layers of interconnected nodes, with each layer processing increasingly complex features of the data. This allows them to identify patterns and relationships that might not be apparent to human analysts. Convolutional networks are a type of neural network that are particularly useful for image and video analysis. They are able to detect features such as edges, corners, and shapes in images and use this information to classify objects in the image. Recent advances in convolutional networks include the development of transfer learning, which allows pre-trained convolutional networks to be used for new image recognition tasks with limited amounts of training data. This has made it possible to build accurate image recognition systems in a wide range of applications, from self-driving cars to medical image analysis. Machine learning and deep learning have rapidly become some of the most transformative technologies of the modern era. They have revolutionized the way we process, analyze, and interpret data. Recent advances in neural networks, convolutional networks, and recurrent networks have expanded the scope of what is possible with these technologies. Deep learning has experienced substantial progress and observed notable patterns in diverse domains. An important advancement is the fusion of deep learning and cloud technology, which enables organizations to create and apply deep learning solutions by leveraging available resources. Deep learning has demonstrated exceptional efficacy in the field of medical image analysis, particularly in the segmentation of anatomical or diseased structures. This technology has proven to be highly beneficial for physicians, aiding them in the process of diagnosis and surgery planning. Deep learning has proven to be advantageous for dialogue systems, a widely-used task in natural language processing. State-of-the-art models are now being employed in both task-oriented and opendomain systems. In addition, deep learning techniques and structures have been utilized in other fields like healthcare, wearable technology, social networks, and others.

Table of Contents

Contents
Preface
1. INTRODUCTION TO AI
2. INTRODUCTION TO MACHINE LEARNING
3. CLASSIFIERS IN MACHINE LEARNING
4. DEEP LEARNING INTRODUCTION
5. DEEP LEARNING: RNNS AND LSTMS
6. NLP AND REINFORCEMENT LEARNING
7. CONVOLUTIONAL NEURAL NETWORKS
8. EMBEDDING AND REPRESENTATION LEARNING
9. MODELS FOR SEQUENCE ANALYSIS
Bibliography
Index