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& Describes the engineering needs addressed by the individual EDA tools and covers EDA from both the provider and user viewpoints. & & Learn the importance of marketing and business trends in the EDA industry. & & The EDA consortium is made up of major corporations including SUN, HP, and Intel.
Eating Disorders Anonymous: The Story of How We Recovered from Our Eating Disorders presents the accumulated experience, strength, and hope of many who have followed a Twelve-Step approach to recover from their eating disorders. Eating Disorders Anonymous (EDA), founded by sober members of Alcoholics Anonymous (AA), have produced a work that emulates the “Big Book” in style and substance. EDA respects the pioneering work of AA while expanding its Twelve-Step message of hope to include those who are religious or seek a spiritual solution, and for those who are not and may be more comfortable substituting “higher purpose” for the traditional “Higher Power.” Further, the EDA approach embraces the development and maintenance of balance and perspective, rather than abstinence, as the goal of recovery. Initial chapters provide clear directions on how to establish a foothold in recovery by offering one of the founder’s story of hope, and collective voices tell why EDA is suitable for readers with any type of problem eating, including: anorexia nervosa, bulimia, binge eating, emotional eating, and orthorexia. The text then explains how to use the Twelve Steps to develop a durable and resilient way of thinking and acting that is free of eating disordered thoughts and behaviors, including how to pay it forward so that others might have hope of recovery. In the second half of the text, individual contributors share their experiences, describing what it was like to have an eating disorder, what happened that enabled them to make a start in recovery, and what it is like to be in recovery. Like the “Big Book,” these stories are in three sections: Pioneers of EDA, They Stopped in Time, and They Lost Nearly All. Readers using the Twelve Steps to recover from other issues will find the process consistent and reinforcing of their experiences, yet the EDA approach offers novel ideas and specific guidance for those struggling with food, weight and body image issues. Letters of support from three, highly-regarded medical professionals and two, well-known recovery advocates offer reassurance that EDA’s approach is consistent with that supported by medical research and standards in the field of eating disorders treatment. Intended as standard reading for members who participate in EDA groups throughout the world, this book is accessible and appropriate for anyone who wants to recover from an eating disorder or from issues related to food, weight, and body image.
Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas Key FeaturesUnderstand the fundamental concepts of exploratory data analysis using PythonFind missing values in your data and identify the correlation between different variablesPractice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python packageBook Description Exploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization. You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence. By the end of this EDA book, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes. What you will learnImport, clean, and explore data to perform preliminary analysis using powerful Python packagesIdentify and transform erroneous data using different data wrangling techniquesExplore the use of multiple regression to describe non-linear relationshipsDiscover hypothesis testing and explore techniques of time-series analysisUnderstand and interpret results obtained from graphical analysisBuild, train, and optimize predictive models to estimate resultsPerform complex EDA techniques on open source datasetsWho this book is for This EDA book is for anyone interested in data analysis, especially students, statisticians, data analysts, and data scientists. The practical concepts presented in this book can be applied in various disciplines to enhance decision-making processes with data analysis and synthesis. Fundamental knowledge of Python programming and statistical concepts is all you need to get started with this book.
Presenting a comprehensive overview of the design automation algorithms, tools, and methodologies used to design integrated circuits, the Electronic Design Automation for Integrated Circuits Handbook is available in two volumes. The second volume, EDA for IC Implementation, Circuit Design, and Process Technology, thoroughly examines real-time logic to GDSII (a file format used to transfer data of semiconductor physical layout), analog/mixed signal design, physical verification, and technology CAD (TCAD). Chapters contributed by leading experts authoritatively discuss design for manufacturability at the nanoscale, power supply network design and analysis, design modeling, and much more. Save on the complete set.