Download or read book Green Business Concepts Methodologies Tools and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2019-02-01 with total page 1718 pages. Available in PDF, EPUB and Kindle. Book excerpt: The issues of sustainability and corporate social responsibility have become vital discussions in many industries within the public and private sectors. In the business realm, incorporating practices that serve the overall community and ecological wellbeing can also allow businesses to flourish economically and socially. Green Business: Concepts, Methodologies, Tools, and Applications is a vital reference source for the latest research findings on the challenges and benefits of implementing sustainability into the core functions of contemporary enterprises, focusing on how green approaches improve operations. Highlighting a range of topics such as corporate sustainability, green enterprises, and circular economy, this multi-volume book is ideally designed for business executives, business and marketing professionals, business managers, academicians, and researchers actively involved in the business industry.

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Download or read book Machine Learning for Algorithmic Trading written by Stefan Jansen and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.

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Download or read book Open Source Law Policy and Practice written by Amanda Brock and published by Oxford University Press. This book was released on 2022-10-10 with total page 605 pages. Available in PDF, EPUB and Kindle. Book excerpt: Open Source Software has seen mass adoption in the last decade and potentially forms the majority of software today. It is realised through legal instruments, private law agreements, licences, governance, and community norms—all of which lead to the sharing of intellectual property and to economic and commercial disruption in technology. Written by world leading Open Source and legal experts, this new edition of Open Source Law, Policy and Practice is fully updated with a global focus on technology and market changes over the last decade. The work delivers an in-depth examination of the community, legal, and commercial structures relating to the usage and exploitation of Open Source. This enables readers to understand the legal environment within which Open Source operates and what is required for its appropriate governance and curation in enterprise and the public sector. This is achieved by focusing on three main areas: intellectual property rights; the governance of Open Source; and the business and economic impacts.

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Download or read book Learn Amazon SageMaker written by Julien Simon and published by Packt Publishing Ltd. This book was released on 2020-08-27 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker’s capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor Key FeaturesBuild, train, and deploy machine learning models quickly using Amazon SageMakerAnalyze, detect, and receive alerts relating to various business problems using machine learning algorithms and techniquesImprove productivity by training and fine-tuning machine learning models in productionBook Description Amazon SageMaker enables you to quickly build, train, and deploy machine learning (ML) models at scale, without managing any infrastructure. It helps you focus on the ML problem at hand and deploy high-quality models by removing the heavy lifting typically involved in each step of the ML process. This book is a comprehensive guide for data scientists and ML developers who want to learn the ins and outs of Amazon SageMaker. You’ll understand how to use various modules of SageMaker as a single toolset to solve the challenges faced in ML. As you progress, you’ll cover features such as AutoML, built-in algorithms and frameworks, and the option for writing your own code and algorithms to build ML models. Later, the book will show you how to integrate Amazon SageMaker with popular deep learning libraries such as TensorFlow and PyTorch to increase the capabilities of existing models. You’ll also learn to get the models to production faster with minimum effort and at a lower cost. Finally, you’ll explore how to use Amazon SageMaker Debugger to analyze, detect, and highlight problems to understand the current model state and improve model accuracy. By the end of this Amazon book, you’ll be able to use Amazon SageMaker on the full spectrum of ML workflows, from experimentation, training, and monitoring to scaling, deployment, and automation. What you will learnCreate and automate end-to-end machine learning workflows on Amazon Web Services (AWS)Become well-versed with data annotation and preparation techniquesUse AutoML features to build and train machine learning models with AutoPilotCreate models using built-in algorithms and frameworks and your own codeTrain computer vision and NLP models using real-world examplesCover training techniques for scaling, model optimization, model debugging, and cost optimizationAutomate deployment tasks in a variety of configurations using SDK and several automation toolsWho this book is for This book is for software engineers, machine learning developers, data scientists, and AWS users who are new to using Amazon SageMaker and want to build high-quality machine learning models without worrying about infrastructure. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. Some understanding of machine learning concepts and the Python programming language will also be beneficial.

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Download or read book Hands on Booting written by Yogesh Babar and published by Apress. This book was released on 2020-06-30 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the booting procedure of various operating systems with in-depth analysis of bootloaders and firmware. The primary focus is on the Linux booting procedure along with other popular operating systems such as Windows and Unix. Hands-on Booting begins by explaining what a bootloader is, starting with the Linux bootloader followed by bootloaders for Windows and Unix systems. Next, you’ll address the BIOS and UEFI firmware by installing multiple operating systems on one machine and booting them through the Linux bootloader. Further, you’ll see the kernel's role in the booting procedure of the operating system and the dependency between kernel, initramfs, and dracut. You’ll also cover systemd, examining its structure and how it mounts the user root filesystem. In the final section, the book explains troubleshooting methodologies such as debugging shells followed by live images and rescue mode. On completing this book, you will understand the booting process of major operating systems such as Linux, Windows, and Unix. You will also know how to fix the Linux booting issues through various boot modes. What You Will Learn Examine the BIOS and UEFI firmware Understanding the Linux boot loader (GRUB)Work with initramfs, dracut, and systemdFix can’t-boot issues on Linux Who This Book Is For Linux users, administrators, and developers.

Download or read book Producing Open Source Software written by Karl Fogel and published by "O'Reilly Media, Inc.". This book was released on 2005-10-07 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: The corporate market is now embracing free, "open source" software like never before, as evidenced by the recent success of the technologies underlying LAMP (Linux, Apache, MySQL, and PHP). Each is the result of a publicly collaborative process among numerous developers who volunteer their time and energy to create better software. The truth is, however, that the overwhelming majority of free software projects fail. To help you beat the odds, O'Reilly has put together Producing Open Source Software, a guide that recommends tried and true steps to help free software developers work together toward a common goal. Not just for developers who are considering starting their own free software project, this book will also help those who want to participate in the process at any level. The book tackles this very complex topic by distilling it down into easily understandable parts. Starting with the basics of project management, it details specific tools used in free software projects, including version control, IRC, bug tracking, and Wikis. Author Karl Fogel, known for his work on CVS and Subversion, offers practical advice on how to set up and use a range of tools in combination with open mailing lists and archives. He also provides several chapters on the essentials of recruiting and motivating developers, as well as how to gain much-needed publicity for your project. While managing a team of enthusiastic developers -- most of whom you've never even met -- can be challenging, it can also be fun. Producing Open Source Software takes this into account, too, as it speaks of the sheer pleasure to be had from working with a motivated team of free software developers.

Download or read book Microsoft Azure Essentials Fundamentals of Azure written by Michael Collier and published by Microsoft Press. This book was released on 2015-01-29 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Microsoft Azure Essentials from Microsoft Press is a series of free ebooks designed to help you advance your technical skills with Microsoft Azure. The first ebook in the series, Microsoft Azure Essentials: Fundamentals of Azure, introduces developers and IT professionals to the wide range of capabilities in Azure. The authors - both Microsoft MVPs in Azure - present both conceptual and how-to content for key areas, including: Azure Websites and Azure Cloud Services Azure Virtual Machines Azure Storage Azure Virtual Networks Databases Azure Active Directory Management tools Business scenarios Watch Microsoft Press’s blog and Twitter (@MicrosoftPress) to learn about other free ebooks in the “Microsoft Azure Essentials” series.