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Survival Guide to Organic Chemistry
The Survival Guide to Organic Chemistry: Bridging the Gap from General Chemistry enables organic chemistry students to bridge the gap between general chemistry and organic chemistry. It makes sense of the myriad of in-depth concepts of organic chemistry, without overwhelming them in the necessary detail often given in a complete organic chemistry text. Here, the topics covered span the entire standard organic chemistry curriculum. The authors describe subjects which require further explanation, offer alternate viewpoints for understanding and provide hands-on practical problems and solutions to help master the material. This text ultimately allows students to apply key ideas from their gener...
Survival Guide to General Chemistry
This work evolved over thirty combined years of teaching general chemistry to a variety of student demographics. The focus is not to recap or review the theoretical concepts well described in the available texts.Instead, the topics and descriptions in this book make available specific, detailed step-by-step methods and procedures for solving the major types of problems in general chemistry. Explanations, instructional process sequences, solved examples and completely solved practice problems are greatly expanded, containing significantly more detail than can usually be devoted to in a comprehensive text. Many chapters also provide alternative viewpoints as an aid to understanding. Key Featur...
Machine Learning with R
Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.
The Science of Science
This is the first comprehensive overview of the exciting field of the 'science of science'. With anecdotes and detailed, easy-to-follow explanations of the research, this book is accessible to all scientists, policy makers, and administrators with an interest in the wider scientific enterprise.
Encyclopedia of Biology
Contains approximately 800 alphabetical entries, prose essays on important topics, line illustrations, and black-and-white photographs.
Learn By Examples - Asian Eastern Recipes [Singapore Food Tour and Learn to Cook Singapore Food]
Have you ever want to tour Singapore and cook Singapore food? If yes, this book is for you. This book aims to be a Singapore Food Tour Guide and Cookbook. This book will guide you from Changi Airport to Bedok Hawker Centre to Orchard Road to Istana Park to Marina Bay Sands to Merlion to Boat Quay to Clarke Quay. This book will guide you to find good food in Singapore. When you go back to your country, this book will teach you how to cook Singapore Food. Contents 1. Singapore Food Tour (95 pages out of 325 pages.) 2. Chinese Food (Fried Rice, Oyster Egg, Steam Fish, Ginger Onion Garlic Stir Fry Chicken,) 3. Malay Food (Sambal Belacan Stir Fry Mussel, Cuttlefish, Razor Calms, Chicken) 4. Indian Food (Indian Curry Chicken) 5. Western Food (Cheese Cake, Chocolate Chips Cookies, Bread, Soy Baked Chicken, ) 6. Korean Food (Kim Chi, Kim Chi Fried Rice) 7. Japanese Food (Japanese Beancurd, Beancurd Kumbu Miso Soup, Udon Soup, ) 8. Thai Food (Tom Kha Gai, Green Curry Paste, Green Curry, Tom Yum Soup) 9. Grow Your Own Food(Grow Beansprouts, Grow Microgreens)
Artificial Intelligence in Performance-Driven Design
ARTIFICIAL INTELLIGENCE IN PERFORMANCE-DRIVEN DESIGN A definitive, interdisciplinary reference to using artificial intelligence technology and data-driven methodologies for sustainable design Artificial Intelligence in Performance-Driven Design: Theories, Methods, and Tools explores the application of artificial intelligence (AI), specifically machine learning (ML), for performance modeling within the built environment. This work develops the theoretical foundations and methodological frameworks for utilizing AI/ML, with an emphasis on multi-scale modeling encompassing energy flows, environmental quality, and human systems. The book examines relevant practices, case studies, and computationa...
Machine Learning in Cardiovascular Medicine
Machine Learning in Cardiovascular Medicine addresses the ever-expanding applications of artificial intelligence (AI), specifically machine learning (ML), in healthcare and within cardiovascular medicine. The book focuses on emphasizing ML for biomedical applications and provides a comprehensive summary of the past and present of AI, basics of ML, and clinical applications of ML within cardiovascular medicine for predictive analytics and precision medicine. It helps readers understand how ML works along with its limitations and strengths, such that they can could harness its computational power to streamline workflow and improve patient care. It is suitable for both clinicians and engineers; providing a template for clinicians to understand areas of application of machine learning within cardiovascular research; and assist computer scientists and engineers in evaluating current and future impact of machine learning on cardiovascular medicine.
Efficient R Programming
There are many excellent R resources for visualization, data science, and package development. Hundreds of scattered vignettes, web pages, and forums explain how to use R in particular domains. But little has been written on how to simply make R work effectively—until now. This hands-on book teaches novices and experienced R users how to write efficient R code. Drawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace provide practical advice on a range of topics—from optimizing the set-up of RStudio to leveraging C++—that make this book a useful addition to any R user’s bookshelf. Academics, business users, and programmers from a wide range of bac...
Statistical Regression and Classification
Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods. * Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case. * In view of the voluminous nature of many modern datasets, there is a chapter on Big Data. * Has special Mathematical and Computation...
