Built To Learn
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How Buildings Learn
A captivating exploration of the ever-evolving world of architecture and the untold stories buildings tell. When a building is finished being built, that isn’t the end of its story. More than any other human artifacts, buildings improve with time—if they’re allowed to. Buildings adapt by being constantly refined and reshaped by their occupants, and in that way, architects can become artists of time rather than simply artists of space. From the connected farmhouses of New England to I.M. Pei’s Media Lab, from the evolution of bungalows to the invention of Santa Fe Style, from Low Road military surplus buildings to a High Road English classic like Chatsworth—this is a far-ranging survey of unexplored essential territory. Discover how structures become living organisms, shaped by the people who inhabit them, and learn how architects can harness the power of time to create enduring works of art through the interconnected worlds of design, function, and human ingenuity.
Second Language Learning
A survey and analysis of second language theory discusses the development of ideas in this expanding area of language studies. It looks at the implications of these ideas and directions for future research. Contains study questions and activities as well as practical guidelines on the use of available research resources.
Deep Learning for Coders with fastai and PyTorch
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