Anthony Chang
Chief Intelligence and Innovation Officer, Children's Hospital of Orange County
Book Recommendations:
Recommended by Anthony Chang
“This volume on biomedical data science is an excellent reference for all those interested in data science in the healthcare domain. Most of the reference books in this domain lack exact relevance for the clinician but this reference is much more relatable. What is very helpful is that one does not have to have programming skills to enjoy the interactive sections of the book. It also has an impressive balance of theory and practice: while it covers essential topics such as overview of biomedical data science (data analytical processes and major types of analytics), biostatistics primer, introduction to databases, and machine learning, it also has chapters on practical topics such as spreadsheet tools and tips as well as programming languages for data analysis. There is also a very helpful section on biomedical data science resources as well as exercises (with step by step instructions) and references for each of the chapters, and this compendium renders the book an ideal companion to the beginner student to the more advanced practitioner.” (from Amazon)
by Robert Hoyt, Robert Muenchen·You?
by Robert Hoyt, Robert Muenchen·You?
Introduction to Biomedical Data Science aims to fill the data science knowledge gap experienced by many clinical, administrative and technical staff. The textbook begins with an overview of what biomedical data science is and then embarks on a tour of topics beginning with spreadsheet tips and tricks and ending with artificial intelligence. In between, important topics are covered such as biostatistics, data visualization, database systems, big data, programming languages, bioinformatics, and machine learning. The textbook is available as a paperback and ebook. Visit the companion website at https://www.informaticseducation.org for more information. Key features: Real healthcare datasets are used for examples and exercises; Knowledge of a programming language or higher math is not required; Multiple free or open source software programs are presented; YouTube videos are embedded in most chapters; Extensive resources chapter for further reading and learning; PowerPoints and an Instructor Manual
Recommended by Anthony Chang
“Jeremy Howard and Sylvain Gugger have authored a bravura of a book that successfully bridges the AI domain with the rest of the world. This work is a singularly substantive and insightful yet absolutely relatable primer on deep learning for anyone who is interested in this domain: a lodestar book amongst many in this genre.” (from Amazon)
by Jeremy Howard, Sylvain Gugger·You?
by Jeremy Howard, Sylvain Gugger·You?
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala