Psychology of Learning for Instruction, Third Edition, focuses on the applications and implications of the learning theories. Using excellent examples ranging from primary school instruction to corporate training, this text combines the latest thinking and research to give readers the opportunity to explore the individual theories as viewed by the experts. Readers are encouraged to apply "reflective practice," which is designed to foster a critical and reflective mode of thinking when considering any particular approach to learning and instruction. Provides readers with the practical knowledge needed to apply learning theories to instruction. KEY TOPICS: This text addresses learning as it relates to behavior, cognition, development, biology, motivation and instruction. MARKET: Pre-service and in-service teachers, and educational psychologists.

    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 book has undergone three editions, several reprints, and revised reprints in a span of 13 years. There are many biochemistry textbooks in the market. Some of them are purely basic while others are applied, and there are very few books which cover both these aspects together. For this reason, the students learning biochemistry in their undergraduate courses have to depend on multiple books to acquire a sound knowledge of the subject. This book, ‘Biochemistry’ is unique with a simultaneous and equal emphasis on basic and applied aspects of biochemistry. This textbook offers an integration of medical and pure sciences, comprehensively written to meet the curriculum requirements of undergraduate courses in medical, dental, pharmacy, life-sciences and other categories (agriculture, veterinary, etc.). This book is designed to develop in students a sustained interest and enthusiasm to learn and develop the concepts in biochemistry in a logical and stepwise manner. It incorporates a variety of pedagogic aids, besides colour illustrations to help the students understand the subject quickly and to the maximum. The summary and biomedical/clinical concepts are intended for a rapid absorption and assimilation of the facts and concepts in biochemistry. The self-assessment exercises will stimulate the students to think rather than merely learn the subject. In addition, these exercises (essays, short notes, fill in the blanks, multiple choice questions) set at different difficulty levels, will cater to the needs of all the categories of learners.New to This Edition The book offers an integration of medical and pure sciences, and is comprehensively written, revised and updated to meet the curriculum requirements of Medical, Pharmacy, Dental, Veterinary, Biotechnology, Agricultural Sciences, Life Sciences, and others studying Biochemistry as one of the subjects. It is the first text book on Biochemistry in English with multi-colour illustrations by an author from Asia. The use of multicolours is for a clearer understanding of the complicated biochemical reactions. It is written in a lucid style with the subject being presented as an engaging story growing from elementary information to the most recent advances, and with theoretical discussions being supplemented with illustrations, flowcharts, and tables for easy understanding of Biochemistry. It has each chapter beginning with a four-line verse followed by the text, biomedical concepts, a summary, and self-assessment exercises. The lively illustrations and text with appropriate headings and sub-headings in bold type faces facilitate reading path clarity and quick recall. It provides the most recent and essential information on Molecular Biology and Biotechnology, Diabetes, Cancer, Free Radicals, Free radicals and Antioxidants, Prostaglandins, etc. It describes a wide variety of case studies and biochemical correlations and several newer biomedical aspects- Metabolic syndrome, Therapeutic diets, Atkins diet, Trans fatty acids, Epigenetics, Nutrigenomics, Recombinant ribozymes, Membrane transport disorders, Pleural fluid etc. It contains the basics (Bioorganic and Biophysical Chemistry, Tools of Biochemistry, Immunology, and Genetics) for beginners to learn easily Biochemistry, origins of biochemical words, confusables in Biochemistry, principles of Practical Biochemistry, and Clinical Biochemistry Laboratory.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    You may find it for free on the web. Purchase of the Kindle edition includes wireless delivery.

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    Offering numerous examples and a step-by-step approach, it covers both the Assembly and C programming languages and devotes separate chapters to interfacing with peripherals such as Timers, LCD, Serial Ports, Interrupts, Motors and more. A unique chapter on hardware design of the PIC system and the PIC trainer round out coverage. Systematic coverage of the PIC18 family of Microcontrollers. Assembly language and C language programming and interfacing techniques. Thorough coverage of Architectures and Assembly language programming of the PIC18. Thorough coverage of C language programming of the PIC18. Separate chapters on programming and interfacing the PIC with peripherals - Includes information on how to interface the PIC with LCD, keyboard, ADC, DAC, Sensors, Serial Ports, Timers, DC and Stepper Motors, Optoisolators, and RTC. Covers how to program each peripheral, first using the Assembly language and then using the C language. Those involved with PIC programming and interfacing using Assembly and C languages."

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    The set includes thirty-one 30-minute MCAT practice exams with answers and explanations and more than 1,600 MCAT questions.

    Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. This book can be used for readers who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.

    The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on a large scale, and the development of a variety of new theoretical tools has allowed us to extract new knowledge from many different kinds of networks.The study of networks is broadly interdisciplinary and important developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.Subjects covered include the measurement and structure of networks in many branches of science, methods for analyzing network data, including methods developed in physics, statistics, and sociology, the fundamentals of graph theory, computer algorithms, and spectral methods, mathematical models of networks, including random graph models and generative models, and theories of dynamical processes taking place on networks.

    Dozens of examples in innumeracy show us how it affects not only personal economics and travel plans, but explains mis-chosen mates, inappropriate drug-testing, and the allure of pseudo-science.

    This approach will captivate students and encourage them to think critically about the nature of public administration today. Introducing Public Administration provides students with a solid, conceptual foundation in public administration, and contains the latest information on important trends in the discipline. To further engage students and deepen interest in its narrative, the text uses unique chapter-opening vignettes called Keynotes, chapter ending case studies, and a series of boxes throughout that offer real-life excerpts and alternative theories.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.