What type of font to use? How big? How should those letters, words, and paragraphs be aligned, spaced, ordered, shaped, and otherwise manipulated? In this groundbreaking new primer, leading design educator and historian Ellen Lupton provides clear and concise guidance for anyone learning or brushing up on their typographic skills. Thinking with Type is divided into three sections: letter, text, and grid. Each section begins with an easy-to-grasp essay that reviews historical, technological, and theoretical concepts, and is then followed by a set of practical exercises that bring the material covered to life. Sections conclude with examples of work by leading practitioners that demonstrate creative possibilities (along with some classic no-no's to avoid).

    This text provides a thorough exposition of the principles of thermodynamics and details their application to chemical processes. The new edition has been updated to reflect the growth in such areas as materials and electrochemicals.

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    Discover how the basic TCP/IP technology has survived and evolved over two decades of exponential growth, and understand the TCP/IP protocols and technical advances. This edition explains emerging technologies such as Mobile IP, Virtual Private Networks, resource reservation with RSVP, and Ipv6. Comer reveals how to master TCP/IP and how the Internet works. The reader is required to have a modest background in the fundamentals of computer systems, but does not need sophisticated mathematics. As with previous editions, this edition provides an introduction to physical networks and then shows how they are combined to form an internet. It states design principles clearly, and discusses motivations and consequences. THIS NEW EDITION OF VOLUME 1: *Explains how voice and video are sent over IP internets and how IP Telephony operates *Describes Mobile IP (a technology that allows a computer to move fr

    Provides a review of introductory noncalculus-based physics for those who do not have a strong background in mathematics.

    It should be labelled 'start here'." --Pieter Jan Stappers, ID-StudioLab, Delft University of Technology

    More importantly, this book provides a framework for thinking about computer organization and design that will enable the reader to continue the lifetime of learning necessary for staying at the forefront of this competitive discipline. --John Crawford Intel Fellow Director of Microprocessor Architecture, Intel The performance of software systems is dramatically affected by how well software designers understand the basic hardware technologies at work in a system. Similarly, hardware designers must understand the far reaching effects their design decisions have on software applications. For readers in either category, this classic introduction to the field provides a deep look into the computer. It demonstrates the relationship between the software and hardware and focuses on the foundational concepts that are the basis for current computer design. Using a distinctive learning by evolution approach the authors present each idea from its first principles, guiding readers through a series of worked examples that incrementally add more complex instructions until they ha

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    You'll get in-depth coverage of the C language and function libraries as well as all the newest C features, including restricted pointers, inline functions, variable-length arrays, and complex math. This jam-packed resource includes hundreds of examples and sample applications.

    Emphasizing the basic concepts of the field, this book covers circuit analysis, digital systems, electronics, and electromechanics. This book develops theoretical and experimental skills and experiences in the following areas: basic circuit analysis and measurement, first- and second-order transients, steady-state ac circuits, resonance and frequency response, digital logic circuits, microcontrollers (68HC11), computer-based instrumentation, diode circuits, electronic amplifiers, field-effect and bipolar junction transistors, operational amplifiers, ac and dc machines, and more. For engineers or any other professionals who need a solid foundation in the basics of circuits, digital systems, analog electronics, and electromechanics.

    These themes include mathematical reasoning, combinatorial analysis, discrete structures, algorithmic thinking, and enhanced problem-solving skills through modeling. Its intent is to demonstrate the relevance and practicality of discrete mathematics to all students. The Fifth Edition includes a more thorough and linear presentation of logic, proof types and proof writing, and mathematical reasoning. This enhanced coverage will provide students with a solid understanding of the material as it relates to their immediate field of study and other relevant subjects. The inclusion of applications and examples to key topics has been significantly addressed to add clarity to every subject. True to the Fourth Edition, the text-specific web site supplements the subject matter in meaningful ways, offering additional material for students and instructors. Discrete math is an active subject with new discoveries made every year. The continual growth and updates to the web site reflect the active nature of the topics being discussed. The book is appropriate for a one- or two-term introductory discrete mathematics course to be taken by students in a wide variety of majors, including computer science, mathematics, and engineering. College Algebra is the only explicit prerequisite.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, 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 models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This 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, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    Jay Withgott and new co-author Matt Laposata present the latest coverage of environmental science and introduce new FAQ sections to address common student misconceptions. Note: This is the standalone book if you want the book/access card order the ISBN below: 0321752546 / 9780321752543 Essential Environment: The Science behind the Stories Plus MasteringEnvironmentalScience with eText -- Access Card Package Package consists of: 0321752902 / 9780321752901 Essential Environment: The Science behind the Stories 0321754077 / 9780321754073 MasteringEnvironmentalScience with Pearson eText -- Valuepack Access Card -- Essential Environment: The Science behind the Stories (ME component) "

    Unnikrishna Pillai of Polytechnic University. The book is intended for a senior/graduate level course in probability and is aimed at students in electrical engineering, math, and physics departments. The authors' approach is to develop the subject of probability theory and stochastic processes as a deductive discipline and to illustrate the theory with basic applications of engineering interest. Approximately 1/3 of the text is new material--this material maintains the style and spirit of previous editions. In order to bridge the gap between concepts and applications, a number of additional examples have been added for further clarity, as well as several new topics.