data driven machine learning applications (20 résultats)

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Paperback or Softback. Etat : New. Building Data-Driven Applications with Danfo.js: A practical guide to data analysis and machine learning using JavaScript. Book.

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Softcover. Etat : neu. 1. Auflage. - in englischer Sprache - Schriftenreihe innovative betriebswirtschaftliche Forschung und Praxis, Band 578 360 pages. -------------------------------------------------------------- ------------------------------------------------------------ ------------------------------------------ REZENSION in: Ãsterreichische Zeitschrift für Kartellrecht, ÃZK 2024 / Heft 5, S. 201-203: "[.] Die Verfügbarkeit unfassbar umfassender und vielfältiger Datensätze (big data) kann sich als Segen und Fluch zugleich erweisen, wie die vorliegende Dissertation eines international agierenden Managers zeigt, der selbst an der Schnittstelle von Naturwissenschaft, Finanzen und prädiktiven Technologien sitzt. [.] Wasserbacher warnt [.] vor [.] einer kritiklosen Anwendung derartiger Techniken und Werkzeuge. Denn ansonst kann es zu fragwürdigen Schlussfolgerungen kommen, sofern dem datengetriebenen Management keine Werkzeuge zur Erstellung passender Kausalfragen und zur Ãberprüfung von deren Anwendung zugrunde liegen, um die entsprechende Anwendungspraxis begleiten und bei Bedarf lenken zu kà nnen. [.] Werden die einschlägigen Werkzeuge quasi "von der Stange" - also ohne sie den konkreten Umständen und Erfordernissen anzupassen - angewandt, so kà nnen die auf ihnen basierenden unternehmerischen Entscheidungen scheitern. [.] Während Wasserbacher die Entstehung der drei Forschungsarbeiten aus dem "Innenleben eines Unternehmens" unterstreicht, sieht er zum Thema KI-Regulierung groÃe Relevanz für die Leser(innnen) der ÃZK, zumal mit dem bereits oben erwähnten EU AI Act "die weltweit erste 'horizontale' KI-Regulierung und Aufsichtspflicht eingeführt" werde, was aber Hintergrundwissen über KI als Technologie erforderlich mache. [.] Die hier vermittelten Einsichten aus der vordersten Front eines Managers kà nnen dabei wertvolle Dienste leisten." --------------------------------------------------------------------- ------------------------------------------------------------ ----------------------------------- The availability of large amounts of data, coupled with artificial intelligence and machine learning as suitable techniques to exploit them, has led to increasing interest in data-driven management. Data are turned into insights, and insights into management decisions. In the midst of this passion for artificial intelligence, practitioners must remain aware that most machine learning methods maximize predictive performance. This is not the same as identifying causal patterns. Outside a valid causal framework, machine learning will lead to flawed conclusions about causal effects, and thus to incorrect decisions. Data-driven management requires appropriate tools for causal questions. This book discusses in-depth three concrete examples in the areas of corporate finance and marketing. In financial forecasting, planning and analysis (FP&A), machine learning appears well suited for the highly automated extraction of information from large amounts of data. However, FP&A practitioners need to distinguish between forecasting tasks and tasks related to planning and resource allocation. Off-the-shelf machine learning typically fails for causal inference and is not suited for planning and resource allocation. In pharma marketing, the field force traditionally plays an important role. However, does a traditional field force still add value in an otherwise digital and virtual marketing mix? To answer this question, the impact of a field force within an omnichannel strategy is evaluated in a business experiment. The third use case applies double machine learning to the capital structure puzzle and credit ratings. Double machine learning performs data-driven variable selection out of a large set of individual company characteristics and models their relationship with leverage and credit ratings without any strong assumption about the underlying functional form. This allows to quantify the causal effect of credit ratings, along the rating scale, on the leverage.

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Taschenbuch. Etat : Neu. Building Data-Driven Applications with [.] | A practical guide to data analysis and machine learning using JavaScript | Rising Odegua (u. a.) | Taschenbuch | Kartoniert / Broschiert | Englisch | 2021 | Packt Publishing | EAN 9781801070850 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Get hands-on with building data-driven applications using Danfo.js in combination with other data analysis tools and techniquesKey Features:Build microservices to perform data transformation and ML model serving in JavaScriptExplore what Danfo.js is and how it helps with data analysis and data visualizationCombine Danfo.js and TensorFlow.js for machine learningBook Description:Most data analysts use Python and pandas for data processing for the convenience and performance these libraries provide. However, JavaScript developers have always wanted to use machine learning in the browser as well. This book focuses on how Danfo.js brings data processing, analysis, and ML tools to JavaScript developers and how to make the most of this library to build data-driven applications.Starting with an overview of modern JavaScript, you'll cover data analysis and transformation with Danfo.js and Dnotebook. The book then shows you how to load different datasets, combine and analyze them by performing operations such as handling missing values and string manipulations. You'll also get to grips with data plotting, visualization, aggregation, and group operations by combining Danfo.js with Plotly. As you advance, you'll create a no-code data analysis and handling system and create-react-app, react-table, react-chart, Draggable.js, and tailwindcss, and understand how to use TensorFlow.js and Danfo.js to build a recommendation system. Finally, you'll build a Twitter analytics dashboard powered by Danfo.js, Next.js, node-nlp, and Twit.js.By the end of this app development book, you'll be able to build and embed data analytics, visualization, and ML capabilities into any JavaScript app in server-side Node.js or the browser.What You Will Learn:Perform data experimentation and analysis with Danfo.js and DnotebookBuild machine learning applications using Danfo.js integrated with TensorFlow.jsConnect Danfo.js with popular database applications to aid data analysisCreate a no-code data analysis and handling system using internal librariesDevelop a recommendation system with Danfo.js and TensorFlow.jsBuild a Twitter analytics dashboard for sentiment analysis and other types of data insightsWho this book is for:This book is for data analysts, data scientists, and JavaScript developers who want to create data-driven applications in the JavaScript/Node.js environment. Intermediate-level knowledge of JavaScript programming and data science using pandas is expected.

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