Quickly learn and employ practical recipes for developing real-world, cross-platform applications using Delphi.
Key Features
Book Description
Delphi is a cross-platform integrated development environment (IDE) that supports rapid application development on different platforms, saving you the pain of wandering amid GUI widget details or having to tackle inter-platform incompatibilities.
Delphi Cookbook begins with the basics of Delphi and gets you acquainted with JSON format strings, XSLT transformations, Unicode encodings, and various types of streams. You'll then move on to more advanced topics such as developing higher-order functions and using enumerators and run-time type information (RTTI). As you make your way through the chapters, you'll understand Delphi RTL functions, use FireMonkey in a VCL application, and cover topics such as multithreading, using aparallel programming library and deploying Delphi on a server. You'll take a look at the new feature of WebBroker Apache modules, join the mobile revolution with FireMonkey, and learn to build data-driven mobile user interfaces using the FireDAC database access framework. This book will also show you how to integrate your apps with Internet of Things (IoT).
By the end of the book, you will have become proficient in Delphi by exploring its different aspects such as building cross-platforms and mobile applications, designing server-side programs, and integrating these programs with IoT.
What you will learn
Who this book is for
Delphi Cookbook is for intermediate developers with a basic knowledge of Delphi who want to discover and understand all the development possibilities offered by it.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Daniele Spinetti is a software architect living in Rome. He is an Embarcadero MVP. Delphi/Object Pascal is his favorite tool/programming language, and he is a lead and active member of several projects in the open source community. In his tutoring activities (conferences and training), he likes to talk about innovative topics in software architectures. He's a huge fan of design patterns and TDD.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Digital. Etat : New. 2nd Revised edition. Harness the power of Python to develop data mining applications, analyze data, delve into machine learning, explore object detection using Deep Neural Networks, and create insightful predictive models.About This Book. Use a wide variety of Python libraries for practical data mining purposes. Learn how to find, manipulate, analyze, and visualize data using Python. Step-by-step instructions on data mining techniques with Python that have real-world applications.Who This Book Is ForIf you are a Python programmer who wants to get started with data mining, then this book is for you. If you are a data analyst who wants to leverage the power of Python to perform data mining efficiently, this book will also help you. No previous experience with data mining is expected.What You Will Learn. Apply data mining concepts to real-world problems. Predict the outcome of sports matches based on past results. Determine the author of a document based on their writing style. Use APIs to download datasets from social media and other online services. Find and extract good features from difficult datasets. Create models that solve real-world problems. Design and develop data mining applications using a variety of datasets. Perform object detection in images using Deep Neural Networks. Find meaningful insights from your data through intuitive visualizations. Compute on big data, including real-time data from the internetIn DetailThis book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK.You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now.With restructured examples and code samples updated for the latest edition of Python, each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will have great insights into using Python for data mining and understanding of the algorithms as well as implementations.Style and approachThis book will be your comprehensive guide to learning the various data mining techniques and implementing them in Python. A variety of real-world datasets is used to explain data mining techniques in a very crisp and easy to understand manner. N° de réf. du vendeur LU-9781787126787
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