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As part of the best selling Pocket Primer series, this book provides an overview of the major aspects and the source code to use the latest versions of Android. It has coverage of the fundamental aspects of Android that are illustrated via code samples for versions 4.x through 7.x and features the Google Pixel phone. This Pocket Primer is primarily for self-directed learners who want to learn Android programming and it serves as a starting point for deeper exploration of its numerous applications. Companion disc (also available for downloading from the publisher) with source code, images, and appendices.Features:*Contains latest material on Android VR, graphics/animation, apps, and features the new Google Pixel phone*Includes companion files with all of the source code, appendices, and images from the book *Provides coverage of the fundamental aspects of Android that are illustrated via code samples for versions 4.x through 7.xOn the Companion Files:* Source code samples* All images from the text (including 4-color)* Appendices (see Table of Contents)
Application software --- Smartphones --- Development. --- Programming. --- Android (Electronic resource) --- Android (Electronic Resource) --- Application Software --- Tablet Computers --- Computers
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AngularJS (Software framework). --- Application software. --- Application software --- Development. --- AngularJS (Software framework)
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Introduces readers to regular expressions in several technologies. The book shows how to create an assortment of regular expressions, such as filtering data for strings containing uppercase or lowercase letters; matching integers, decimals, hexadecimal, and scientific numbers; and context-dependent pattern matching expressions.
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As part of thebest-selling Pocket Primerseries, thisbook is designed to introducebeginners to basic machine learning algorithms using TensorFlow 2. It isintended to be a fast-paced introduction to various "core" features ofTensorFlow, with code samples that cover machine learning and TensorFlowbasics. A comprehensive appendix contains someKeras-based code samples and the underpinnings of MLPs, CNNs, RNNs, and LSTMs. The material inthe chapters illustrates how to solve a variety of tasks after which you can dofurther reading to deepen your knowledge. Companion files with all of the codesamples are available for downloading from the publisher by emailing proof of purchase to info@merclearning.com.Features:Uses Python for codesamplesCovers TensorFlow 2 APIsand DatasetsIncludes a comprehensiveappendix that covers Keras and advanced topics such as NLPs, MLPs, RNNs, LSTMsFeatures the companion files with all of thesource code examples and figures (download fromthe publisher)
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As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to the basic concepts of data science using Python 3 and other computer applications. It is intended to be a fast-paced introduction to some basic features of data analytics and also covers statistics, data visualization, linear algebra, and regular expressions. The book includes numerous code samples using Python, NumPy, R, SQL, NoSQL, and Pandas. Companion files with source code and color figures are available. FEATURES: Includes a concise introduction to Python 3 and linear algebra; Provides a thorough introduction to data visualization and regular expressions; Covers NumPy, Pandas, R, and SQL; Introduces probability and statistical concepts; Features numerous code samples throughout; Companion files with source code and figures.
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This book is designed to provide the reader with basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fundamental concepts of machine learning. The sixth chapter is devoted to machine learning classifiers, such as logistic regression, k-NN, decision trees, random forests, and SVMs. The final chapter includes material on NLP and RL. Keras-based code samples are included to supplement the theoretical discussion. The book also contains separate appendices for regular expressions, Keras, and TensorFlow 2. -- Provided by publisher.
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Das Insolvenzplanverfahren als Sanierungsinstrument gewinnt mit zunehmender Globalisierung immer mehr an Bedeutung. Gerade in der kritischen Phase nach Aufhebung der Insolvenz zum Übergang in die Planüberwachung ist der Sanierungserfolg der Gesellschaft jedoch abhängig von der Zuführung neuer Liquidität. Die vorliegende Untersuchung arbeitet in diesem Zusammenhang den Begriff des Sanierungskredits sowie die speziellen Risiken für Banken heraus und zeigt eine Herangehensweise zur Minimierung ihrer Haftungsgefahren. Hierbei hinterfragt die Autorin insbesondere, ob auf ein zeit- und kostenintensives Sanierungsgutachten zum Zeitpunkt nach Aufhebung des Insolvenzplanverfahrens ausnahmsweise verzichtet werden kann. Inhalt Rechtliche Risiken des Kreditgebers bei Sanierungskrediten Konzept zur Minimierung der Haftungsrisiken Reichweite der gerichtlichen Entscheidungskompetenz im Insolvenzplanverfahren Entbehrlichkeit des Sanierungsgutachtens nach Aufhebung des Insolvenzverfahrens? Die Zielgruppen Dozenten und Studenten der Rechtswissenschaften mit den Schwerpunkten Wirtschafts-, Insolvenz- und Bankrecht Fach- und Führungskräfte in den Bereichen Insolvenzrecht, Banking/Kreditgeschäfte und Unternehmensbewertung Die Autorin Dr. Antje Oswald ist Zivilrichterin am Amtsgericht in Frankfurt am Main und war zuvor als Associate in einer internationalen Wirtschaftskanzlei in den Bereichen Banking, Restrukturierung und Insolvenzrecht tätig. Ihre Promotion erfolgte auf den Gebieten des Gesellschafts- und Versicherungsrechts.
Public finance. --- Financial crises. --- Banks and banking. --- Public Economics. --- Financial Crises. --- Banking.
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As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to the basic concepts of managing data using a variety of computer languages and applications. It is intended to be a fast-paced introduction to some basic features of data management and covers statistical concepts, data-related techniques, features of Pandas, RDBMS, SQL, NLP topics, Matplotlib, and data visualization. Companion files with source code and color figures are available. FEATURES: Covers Pandas, RDBMS, NLP, data cleaning, SQL, and data visualization. Introduces probability and statistical concepts. Features numerous code samples throughout. Includes companion files with source code and figures.
Quantitative research --- Reliability. --- Data processing. --- NLP. --- Pandas. --- RDBMS. --- SQL. --- computer science. --- data analytics. --- data cleaning. --- data visualization. --- programming. --- python. --- statistics. --- Electronic data processing. --- Databases. --- Punched card systems.
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