eTextbook rentals and open access publications are just a few of the ways we support accessibility in education
As summer slips away, we turn our eyes to one of our favorite times of year: back to school. A new school year means new opportunities and, more importantly, new scholarship to read and explore. The MIT Press values open access and student-centric texts that make learning more accessible and affordable—including rentals across our textbook catalog.
To celebrate back to school, we are highlighting just a few of our recent textbooks and handbooks for high-level learners. And to learn more about our eTextbook rental program—and explore further discounts for Inclusive Access programs at participating universities—reach out to our textbook team at mitpress_textbooks@mit.edu.
Understanding Deep Learning by Simon J. D. Prince
Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.
Probabilistic Machine Learning: Advanced Topics by Kevin P. Murphy
An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.
Life in Media: A Global Introduction to Media Studies by Mark Deuze
From the intimate to the mundane, most aspects of our lives—how we learn, love, work, and play—take place in media. Taking an expansive, global perspective, this introductory textbook covers what it means to live in, rather than with, media. Mark Deuze focuses on the lived experience—how people who use smartphones, the internet, and television sets make sense of their digital environment—to investigate the broader role of media in society and everyday life. Life in Media uses relatable examples and case studies from around the world to illustrate the foundational theories, concepts, and methods of media studies. By deliberately including diverse voices and radically embracing the everyday and mundane aspects of media life, this book innovates ways to teach and talk about media.
Essentials of Compilation: An Incremental Approach in Python by Jeremy G. Siek
Compilers are notoriously difficult programs to teach and understand. Most books about compilers dedicate one chapter to each progressive stage, a structure that hides how language features motivate design choices. By contrast, this innovative textbook provides an incremental approach that allows students to write every single line of code themselves. Jeremy Siek guides the reader in constructing their own compiler in the powerful object-oriented programming language Python, adding complex language features as the book progresses. Essentials of Compilation explains the essential concepts, algorithms, and data structures that underlie modern compilers and lays the groundwork for future study of advanced topics. Already in wide use by students and professionals alike, this rigorous but accessible book invites readers to learn by doing.
The Little Learner: A Straight Line to Deep Learning by Daniel P. Friedman and Anurag Mendhekar
The Little Learner introduces deep learning from the bottom up, inviting students to learn by doing. With the characteristic humor and Socratic approach of classroom favorites The Little Schemer and The Little Typer, this kindred text explains the workings of deep neural networks by constructing them incrementally from first principles using little programs that build on one another. Starting from scratch, the reader is led through a complete implementation of a substantial application: a recognizer for noisy Morse code signals. Example-driven and highly accessible, The Little Learner covers all of the concepts necessary to develop an intuitive understanding of the workings of deep neural networks, including tensors, extended operators, gradient descent algorithms, artificial neurons, dense networks, convolutional networks, residual networks, and automatic differentiation.
Structure and Interpretation of Computer Programs: JavaScript Edition by Harold Abelson, Gerald Jay Sussman, Martin Henz, and Tobias Wrigstad
Since the publication of its first edition in 1984 and its second edition in 1996, Structure and Interpretation of Computer Programs (SICP) has influenced computer science curricula around the world. Widely adopted as a textbook, the book has its origins in a popular entry-level computer science course taught by Harold Abelson and Gerald Jay Sussman at MIT. SICP introduces the reader to central ideas of computation by establishing a series of mental models for computation. Earlier editions used the programming language Scheme in their program examples. This edition has been adapted to JavaScript.
Introduction to Algorithms, Fourth Edition by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein
Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers, with self-contained chapters and algorithms in pseudocode. Since the publication of the first edition, Introduction to Algorithms has become the leading algorithms text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout, with new chapters on matchings in bipartite graphs, online algorithms, and machine learning, and new material on such topics as solving recurrence equations, hash tables, potential functions, and suffix arrays.
Graphic Artists Guild Handbook, 16th Edition: Pricing & Ethical Guidelines by The Graphic Artists Guild
For forty-eight years, the Graphic Artists Guild Handbook has been the industry bible for graphic arts professionals. This sixteenth edition represents the most ambitious revision and redesign in over a decade, providing both artists and clients the very latest information on business, ethical, and legal issues. As the graphic art marketplace continues to evolve to meet the needs of both digital and print media, the new Handbook offers professionals an essential guide for keeping up with rapidly changing technology. For the sixteenth edition, the content has been reorganized, topics have been expanded, and new chapters have been added to create a resource that is more relevant to how graphic artists work today.