From the creator of the wildly popular webcomic xkcd, hilarious and informative answers to important questions you probably never thought to ask.
Millions of people visit xkcd.com each week to read Randall Munroe’s iconic webcomic. His stick-figure drawings about science, technology, language, and love have a large and passionate following.
Fans of xkcd ask Munroe a lot of strange questions. What if you tried to hit a baseball pitched at 90 percent the speed of light? How fast can you hit a speed bump while driving and live? If there was a robot apocalypse, how long would humanity last?
In pursuit of answers, Munroe runs computer simulations, pores over stacks of declassified military research memos, solves differential equations, and consults with nuclear reactor operators. His responses are masterpieces of clarity and hilarity, complemented by signature xkcd comics. They often predict the complete annihilation of humankind, or at least a really big explosion.
The book features new and never-before-answered questions, along with updated and expanded versions of the most popular answers from the xkcd website. What If? will be required reading for xkcd fans and anyone who loves to ponder the hypothetical.

Advice to a young scientist by Peter Medawar (Basic Books). The titan of 20th century molecular medicine gives well-meaning and funny (in the British sense) advice on (from the chapter headings): How do I tell if I am cut out to be a scientific research worker? - What shall I do research on? - How can I equip myself to be a scientist or a better one? - Women in science - Aspects of scientific life and manners - Of younger and older scientists - Presentations - Experiment and discovery - Prizes and rewards - The scientific process. Some details of the 25-year old book feel out of date now but it still is an excellent companion up to your first faculty position. See the following quote: "A novice must stick it out until he discovers whether the rewards and compensations of the scientific life are for him comensurate with the disappointments and the toil; but if once a scientist experiences the exhilaration of discovery [...] then he is hooked, and no other kind of life will do."

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for "wide" data (p bigger than n), including multiple testing and false discovery rates.

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.
This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.

"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, co-chair of OpenAI; co-founder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

The development of microscopy revolutionized the world of cell and molecular biology as we once knew it and will continue to play an important role in future discoveries. Bioimaging: Current Concepts in Light and Electron Microscopy is the optimal text for any undergraduate or graduate bioimaging course, and will serve as an important reference tool for the research scientist. This unique text covers, in great depth, both light and electron microscopy, as well as other structure and imaging techniques like x-ray crystallography and atomic force microscopy. Written in a user-friendly style and covering a broad range of topics, Bioimaging describes the state-of-the-art technologies that have powered the field to the forefront of cellular and molecular biological research.

The past year has been one of viral panic – panic about viruses, that is. Through headlines, public health warnings, and at least one homemade hazmat suit, we were reminded of the powerful force of viruses. They are the smallest living things known to science, yet they can hold the entire planet in their sway.
A Planet of Viruses is Carl Zimmer's eye-opening look at the hidden world of viruses. Zimmer, the popular science writer and author of National Geographic's award-winning blog The Loom, has updated this edition to include the stories of new outbreaks, such as Ebola, MERS, and chikungunya virus; new scientific discoveries, such as a hundred-million-year-old virus that infected the common ancestor of armadillos, elephants, and humans; and new findings that show why climate change may lead to even deadlier outbreaks. Zimmer's lucid explanations and fascinating stories demonstrate how deeply humans and viruses are intertwined. Viruses helped give rise to the first life-forms, are responsible for many of our most devastating diseases, and will continue to control our fate for centuries. Thoroughly readable, and as reassuring as it is frightening, A Planet of Viruses is a fascinating tour of a formidable hidden world.

Are you the innovative type, the cook who marches to a different drummer -- used to expressing your creativity instead of just following recipes? Are you interested in the science behind what happens to food while it's cooking? Do you want to learn what makes a recipe work so you can improvise and create your own unique dish?
More than just a cookbook, Cooking for Geeks applies your curiosity to discovery, inspiration, and invention in the kitchen. Why is medium-rare steak so popular? Why do we bake some things at 350 F/175 C and others at 375 F/190 C? And how quickly does a pizza cook if we overclock an oven to 1,000 F/540 C? Author and cooking geek Jeff Potter provides the answers and offers a unique take on recipes -- from the sweet (a "mean" chocolate chip cookie) to the savory (duck confit sugo).
This book is an excellent and intriguing resource for anyone who wants to experiment with cooking, even if you don't consider yourself a geek.
Initialize your kitchen and calibrate your tools
Learn about the important reactions in cooking, such as protein denaturation, Maillard reactions, and caramelization, and how they impact the foods we cook
Play with your food using hydrocolloids and sous vide cooking
Gain firsthand insights from interviews with researchers, food scientists, knife experts, chefs, writers, and more, including author Harold McGee, TV personality Adam Savage, chemist Hervé This, and xkcd

Mathematical Models in Biology is an introductory book for readers interested in biological applications of mathematics and modeling in biology. Connections are made between diverse biological examples linked by common mathematical themes, exploring a variety of discrete and continuous ordinary and partial differential equation models. Although great advances have taken place in many of the topics covered, the simple lessons contained in Mathematical Models in Biology are still important and informative. Shortly after the first publication of Mathematical Models in Biology, the genomics revolution turned Mathematical Biology into a prominent area of interdisciplinary research. In this new millennium, biologists have discovered that mathematics is not only useful, but indispensable! As a result, there has been much resurgent interest in, and a huge expansion of, the fields collectively called mathematical biology. This book serves as a basic introduction to concepts in deterministic biological modeling.

Provides avenues for applying functional analysis to the practical study of natural sciences as well as mathematics. Contains worked problems on Hilbert space theory and on Banach spaces and emphasizes concepts, principles, methods and major applications of functional analysis.

This book introduces the mathematics that supports advanced computer programming and the analysis of algorithms. The primary aim of its well-known authors is to provide a solid and relevant base of mathematical skills - the skills needed to solve complex problems, to evaluate horrendous sums, and to discover subtle patterns in data. It is an indispensable text and reference not only for computer scientists - the authors themselves rely heavily on it! - but for serious users of mathematics in virtually every discipline. Concrete Mathematics is a blending of CONtinuous and disCRETE mathematics. "More concretely," the authors explain, "it is the controlled manipulation of mathematical formulas, using a collection of techniques for solving problems." The subject matter is primarily an expansion of the Mathematical Preliminaries section in Knuth's classic Art of Computer Programming, but the style of presentation is more leisurely, and individual topics are covered more deeply. Several new topics have been added, and the most significant ideas have been traced to their historical roots. The book includes more than 500 exercises, divided into six categories.Complete answers are provided for all exercises, except research problems, making the book particularly valuable for self-study. Major topics include: *Sums *Recurrences *Integer functions *Elementary number theory *Binomial coefficients *Generating functions *Discrete probability *Asymptotic methods This second edition includes important new material about mechanical summation. In response to the widespread use of the first edition as a reference book, the bibliography and index have also been expanded, and additional nontrivial improvements can be found on almost every page. Readers will appreciate the informal style of Concrete Mathematics. Particularly enjoyable are the marginal graffiti contributed by students who have taken courses based on this material. The authors want to convey not only the importance of the techniques presented, but some of the fun in learning and using them. 0201558025B04062001

Widely acclaimed algebra text. This book is designed to give the reader insight into the power and beauty that accrues from a rich interplay between different areas of mathematics. The book carefully develops the theory of different algebraic structures, beginning from basic definitions to some in-depth results, using numerous examples and exercises to aid the reader's understanding. In this way, readers gain an appreciation for how mathematical structures and their interplay lead to powerful results and insights in a number of different settings.
* The emphasis throughout has been to motivate the introduction and development of important algebraic concepts using as many examples as possible.

认知神经科学中的事件相关电位(ERP)技术使得科学家们能够观察到反映各种认知过程的人类脑活动。在《事件相关电位基础》一书中，Steven LuckX寸认知神经科学领域以及相关领域，包捂晴绪神经科学领域和实验精神病理学领域中的实用ERP实验问题，提供了最可充分理解的指导。本书可用作教学与实验的指南。对于那些虽然自己不进行ERP研究，但却需要理解和评价文献中ERP成分的研究者，本书也可用作参考。这本书总结了ERP理论与实践的积累，由此提供了怎样设计、进行，乃至解释ERP实验的详尽指导。这本书还介绍了所需要的理论背景，用以理解一个实验为什么会以某种方式完成。Luck集中地讨论了全世界很多先进ERP实验室所用的那些最基本的技术，这些技术反映了电生理记录的长期发展历史，并为更先进的方法提供了良好的基础。

For Computer Organization and Architecture and Computer Systems courses in CS and EE and ECE departments. Developed out of an introductory course at Carnegie Mellon University, this text explains the important and enduring concepts underlying all computer systems, and shows the concrete ways that these ideas affect the correctness, performance, and utility of application programs. The text's concrete and hands-on approach will help students understand what is going on "under the hood" of a computer system.

Few students studying computer science or computer engineering will ever have the opportunity to build a computer system. On the other hand, most students will be required to use and program computers on a near daily basis. 'Computer Systems' introduces the important and enduring concepts that underlie application programs.