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Links to Free Computer, Mathematics, Technical Books all over the World
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- Title Silverlight for Windows Phone
- Author(s) Charles Petzold
- Publisher: Microsoft Press; 1 edition (October 15, 2010)
- Paperback: N/A
- eBook PDF, 156 page, 8.2 MB
- Language: English
- ISBN-10: N/A
- ISBN-13: N/A
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This e-book is written for those who want to get to know, use, and develop applications for Windows Phone, Microsoft's latest mobile platform. Of course, it would be naive to consider that this e-book covers the topic about Windows Phone entirely, but it can undoubtedly give you a good basic to learn. In this e-book you will not find topics that require advanced hardware supports such as multi-touch or FM, because this e-book is written based on the available emulator. Topics covered: Windows Phone Overview, Using Windows Phone Development Tools, Silverlight on Windows Phone, Specific Features on Windows Phone, Developing a Simple Windows Phone Application.
About the Authors- N/A
- Windows Phone Development and Programming
- Mobile Devices Development and Programming
- Web Frameworks
- Microsoft Windows Programming
- Special Topics
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Windows Phone 8 Programming in C# (Rob Miles)
You'll learn Windows Phone 8.1 programming by doing as you build five apps, covering a range of scenarios, from media playback to hosted HTML to accessing geolocation data and mapping to extending your Windows Phone apps, etc.
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Windows Phone 8 Development Succinctly (Matteo Pagani)
With this book, you'll go from creating a 'Hello World' app to managing network data usage, enabling users to talk to your application through speech APIs, and earning money through in-app purchases.
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Foundations of Machine Learning (Mehryar Mohri, et al)
This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.
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Machine Learning Yearning (Andrew Ng)
You will learn how to align on ML strategies in a team setting, as well as how to set up development (dev) sets and test sets. After finishing this book, you will have a deep understanding of how to set technical direction for a machine learning project.
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Dive into Deep Learning (Aston Zhang, et al.)
This is an open source, interactive book provided in a unique form factor that integrates text, mathematics and code, now supports the TensorFlow, PyTorch, and Apache MXNet programming frameworks, drafted entirely through Jupyter notebooks.
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Understanding Machine Learning: From Theory to Algorithms
Explains the principles behind the automated learning approach and the considerations underlying its usage. Provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations.
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Machine Learning from Scratch (Danny Friedman)
This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers. Those entering the field of machine learning should feel comfortable with this toolbox.
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Reinforcement Learning: An Introduction, Second Edition
It provides a clear and simple account of the key ideas and algorithms of reinforcement learning that is accessible to readers in all the related disciplines. Focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes.
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Probabilistic Machine Learning: An Introduction (Kevin Murphy)
This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.
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