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A Whole Foods Primer Volume 1 Of 2 Easyread Super Large 24pt Edition Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of A Whole Foods Primer Volume 1 Of 2 Easyread Super Large 24pt Edition book. This book definitely worth reading, it is an incredibly well-written.
Author : Anonim
Publisher : ReadHowYouWant.com
Page : 382 pages
File Size : 46,9 Mb
Release : 2024-07-02
Category : Electronic
ISBN : 9781442969728
Author : Beatrice Trum Hunter
Publisher : Readhowyouwant
Page : 380 pages
File Size : 42,7 Mb
Release : 2009-04-10
Category : Health & Fitness
ISBN : 1442969660
A Whole Foods Primer demonstrates the wisdom of selecting whole foods for optimal intake of the nutrients essential for good health. In this book, readers will learn about the basic nutritional components of each whole food group and their specific functions and beneficial roles in the body. The text presents nutrient profiles and surprising facts about some hundred whole foods, their historical uses as food and medicine, and the latest research that identifies their health-bestowing qualities.
Author : Anonim
Publisher : ReadHowYouWant.com
Page : 550 pages
File Size : 52,8 Mb
Release : 2024-07-02
Category : Electronic
ISBN : 9781442969711
Author : Jeffery L. Coffer
Publisher : Woodhead Publishing
Page : 442 pages
File Size : 40,8 Mb
Release : 2021-09-14
Category : Technology & Engineering
ISBN : 9780323851312
In its second, extensively revised second edition, Semiconducting Silicon Nanowires for Biomedical Applications reviews the fabrication, properties, and biomedical applications of this key material. The book begins by reviewing the basics of growth, characterization, biocompatibility, and surface modification of semiconducting silicon nanowires. Attention then turns to use of these structures for tissue engineering and delivery applications, followed by detection and sensing. Reflecting the evolution of this multidisciplinary subject, several new key topics are highlighted, including our understanding of the cell-nanowire interface, latest advances in associated morphologies (including silicon nanoneedles and nanotubes for therapeutic delivery), and significantly, the status of silicon nanowire commercialization in biotechnology. Semiconducting Silicon Nanowires for Biomedical Applications is a comprehensive resource for biomaterials scientists who are focused on biosensors, drug delivery, and the next generation of nano-biotech platforms that require a detailed understanding of the cell-nanowire interface, along with researchers and developers in industry and academia who are concerned with nanoscale biomaterials, in particular electronically-responsive structures. Reviews the growth, characterization, biocompatibility, and surface modification of semiconducting silicon nanowires Describes silicon nanowires for tissue engineering and delivery applications, including cellular binding & internalization, tissue engineering scaffolds, mediated differentiation of stem cells, and silicon nanoneedles & nanotubes for delivery of small molecule / biologic-based therapeutics Highlights the use of silicon nanowires for detection and sensing Presents a detailed description of our current understanding of the cell-nanowire interface Covers the current status of commercial development of silicon nanowire-based platforms
Author : Steven Heller
Publisher : Simon and Schuster
Page : 999 pages
File Size : 49,5 Mb
Release : 2017-09-26
Category : Design
ISBN : 9781621536154
More Than Sixty Course Syllabi That Bring the New Complexity of Graphic Design to Light All graphic designers teach, yet not all graphic designers are teachers. Teaching is a special skill requiring talent, instinct, passion, and organization. But while talent, instinct, and passion are inherent, organization must be acquired and can usually be found in a syllabus. Teaching Graphic Design, Second Edition, contains syllabi that are for all practicing designers and design educators who want to enhance their teaching skills and learn how experienced instructors and professors teach varied tools and impart the knowledge needed to be a designer in the current environment. This second edition is newly revised to include more than thirty new syllabi by a wide range of professional teachers and teaching professionals who address the most current concerns of the graphic design industry, including product, strategic, entrepreneurial, and data design as well as the classic image, type, and layout disciplines. Some of the new syllabi included are: Expressive Typography Designer as Image Maker Emerging Media Production Branding Corporate Design Graphic Design and Visual Culture Impact! Design for Social Change And many more Beginning with first through fourth year of undergraduate courses and ending with a sampling of graduate school course options, Teaching Graphic Design, Second Edition, is the most comprehensive collection of courses for graphic designers of all levels.
Author : Yves Hilpisch
Publisher : "O'Reilly Media, Inc."
Page : 750 pages
File Size : 50,8 Mb
Release : 2014-12-11
Category : Computers
ISBN : 9781491945384
The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance. Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include: Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies
Author : Songjun Li,Yi Ge,Sergey A. Piletsky,Joe Lunec
Publisher : Elsevier
Page : 390 pages
File Size : 41,6 Mb
Release : 2012-07-30
Category : Science
ISBN : 9780444563316
Molecular imprinting is a rapidly growing field with wide-ranging applications, especially in the area of sensor development, where the process leads to improved sensitivity, reliability, stability, and reproducibility in sensing materials. Molecularly Imprinted Sensors in Analytical Chemistry addresses the most recent advances and challenges relating to molecularly imprinted polymer sensors, and is the only book to compile this information in a single source. From fundamentals to applications, this material will be valuable to researchers working in sensing technologies for pharmaceutical separation and chemical analysis, environmental monitoring and protection, defense and security, and healthcare. Provides a systematic introduction to the different types of MIP-based sensors and reviews the basic principles behind each type of sensor Includes state-of-the-art methodology supported by comparisons and discussions from leading experts in the field Covers all types of sensing modes (optical, electrochemical, thermal, acoustic, etc.), materials and platforms Appeals to a multidisciplinary audience of scientists and graduate students in a wide variety of fields, including chemistry, biology, biomedical science and engineering, and materials science and engineering
Author : Eryk Lewinson
Publisher : Packt Publishing Ltd
Page : 426 pages
File Size : 42,6 Mb
Release : 2020-01-31
Category : Computers
ISBN : 9781789617320
Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key FeaturesUse powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial dataExplore unique recipes for financial data analysis and processing with PythonEstimate popular financial models such as CAPM and GARCH using a problem-solution approachBook Description Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach. What you will learnDownload and preprocess financial data from different sourcesBacktest the performance of automatic trading strategies in a real-world settingEstimate financial econometrics models in Python and interpret their resultsUse Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessmentImprove the performance of financial models with the latest Python librariesApply machine learning and deep learning techniques to solve different financial problemsUnderstand the different approaches used to model financial time series dataWho this book is for This book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.
Author : Yves Hilpisch
Publisher : "O'Reilly Media, Inc."
Page : 478 pages
File Size : 40,6 Mb
Release : 2020-10-14
Category : Business & Economics
ISBN : 9781492055389
The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI) Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practice Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets Identify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategies Understand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about
Author : A. W. Joshi
Publisher : Unknown
Page : 306 pages
File Size : 55,9 Mb
Release : 2018
Category : Electronic
ISBN : 9386070944
Author : James Ma Weiming
Publisher : Packt Publishing Ltd
Page : 340 pages
File Size : 42,8 Mb
Release : 2015-04-29
Category : Computers
ISBN : 9781784397876
If you are an undergraduate or graduate student, a beginner to algorithmic development and research, or a software developer in the financial industry who is interested in using Python for quantitative methods in finance, this is the book for you. It would be helpful to have a bit of familiarity with basic Python usage, but no prior experience is required.
Author : Yuxing Yan
Publisher : Packt Publishing Ltd
Page : 653 pages
File Size : 40,8 Mb
Release : 2014-04-25
Category : Computers
ISBN : 9781783284382
A hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python. Python for Finance is perfect for graduate students, practitioners, and application developers who wish to learn how to utilize Python to handle their financial needs. Basic knowledge of Python will be helpful but knowledge of programming is necessary.
Author : Krish Naik
Publisher : Unknown
Page : 378 pages
File Size : 55,9 Mb
Release : 2019-03-29
Category : Computers
ISBN : 1789346371
Learn and implement quantitative finance using popular Python libraries like NumPy, pandas, and Keras Key Features Understand Python data structure fundamentals and work with time series data Use popular Python libraries including TensorFlow, Keras, and SciPy to deploy key concepts in quantitative finance Explore various Python programs and learn finance paradigms Book Description Python is one of the most popular languages used for quantitative finance. With this book, you'll explore the key characteristics of Python for finance, solve problems in finance, and understand risk management. The book starts with major concepts and techniques related to quantitative finance, and an introduction to some key Python libraries. Next, you'll implement time series analysis using pandas and DataFrames. The following chapters will help you gain an understanding of how to measure the diversifiable and non-diversifiable security risk of a portfolio and optimize your portfolio by implementing Markowitz Portfolio Optimization. Sections on regression analysis methodology will help you to value assets and understand the relationship between commodity prices and business stocks. In addition to this, you'll be able to forecast stock prices using Monte Carlo simulation. The book will also highlight forecast models that will show you how to determine the price of a call option by analyzing price variation. You'll also use deep learning for financial data analysis and forecasting. In the concluding chapters, you will create neural networks with TensorFlow and Keras for forecasting and prediction. By the end of this book, you will be equipped with the skills you need to perform different financial analysis tasks using Python What you will learn Clean financial data with data preprocessing Visualize financial data using histograms, color plots, and graphs Perform time series analysis with pandas for forecasting Estimate covariance and the correlation between securities and stocks Optimize your portfolio to understand risks when there is a possibility of higher returns Calculate expected returns of a stock to measure the performance of a portfolio manager Create a prediction model using recurrent neural networks (RNN) with Keras and TensorFlow Who this book is for This book is ideal for aspiring data scientists, Python developers and anyone who wants to start performing quantitative finance using Python. You can also make this beginner-level guide your first choice if you're looking to pursue a career as a financial analyst or a data analyst. Working knowledge of Python programming language is necessary.
Author : Linda Elder,Richard Paul
Publisher : Rowman & Littlefield
Page : 55 pages
File Size : 40,5 Mb
Release : 2019-06-01
Category : Education
ISBN : 9781538133767
The Aspiring Thinker’s Guide to Critical Thinking introduces concepts and strategies for developing essential reasoning skills and intellectual character. As students advance in their academic studies and encounter new situations in their lives, they must learn to differentiate fact from fiction and make decisions based in good reasoning. They must learn to be clear, accurate, relevant, logical, and fair when expressing ideas. This book lays out a clear framework for guiding this development and encouraging lifelong intellectual curiosity. As part of the Thinker’s Guide Library, this book advances the mission of the Foundation for Critical Thinking to promote fairminded critical societies through cultivating essential intellectual abilities and virtues across every field of study across world.
Author : James Ma Weiming
Publisher : Packt Publishing Ltd
Page : 414 pages
File Size : 40,8 Mb
Release : 2019-04-30
Category : Computers
ISBN : 9781789345278
Take your financial skills to the next level by mastering cutting-edge mathematical and statistical financial applications Key FeaturesExplore advanced financial models used by the industry and ways of solving them using PythonBuild state-of-the-art infrastructure for modeling, visualization, trading, and moreEmpower your financial applications by applying machine learning and deep learningBook Description The second edition of Mastering Python for Finance will guide you through carrying out complex financial calculations practiced in the industry of finance by using next-generation methodologies. You will master the Python ecosystem by leveraging publicly available tools to successfully perform research studies and modeling, and learn to manage risks with the help of advanced examples. You will start by setting up your Jupyter notebook to implement the tasks throughout the book. You will learn to make efficient and powerful data-driven financial decisions using popular libraries such as TensorFlow, Keras, Numpy, SciPy, and sklearn. You will also learn how to build financial applications by mastering concepts such as stocks, options, interest rates and their derivatives, and risk analytics using computational methods. With these foundations, you will learn to apply statistical analysis to time series data, and understand how time series data is useful for implementing an event-driven backtesting system and for working with high-frequency data in building an algorithmic trading platform. Finally, you will explore machine learning and deep learning techniques that are applied in finance. By the end of this book, you will be able to apply Python to different paradigms in the financial industry and perform efficient data analysis. What you will learnSolve linear and nonlinear models representing various financial problemsPerform principal component analysis on the DOW index and its componentsAnalyze, predict, and forecast stationary and non-stationary time series processesCreate an event-driven backtesting tool and measure your strategiesBuild a high-frequency algorithmic trading platform with PythonReplicate the CBOT VIX index with SPX options for studying VIX-based strategiesPerform regression-based and classification-based machine learning tasks for predictionUse TensorFlow and Keras in deep learning neural network architectureWho this book is for If you are a financial or data analyst or a software developer in the financial industry who is interested in using advanced Python techniques for quantitative methods in finance, this is the book you need! You will also find this book useful if you want to extend the functionalities of your existing financial applications by using smart machine learning techniques. Prior experience in Python is required.