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Links to Free Computer, Mathematics, Technical Books all over the World
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- Title 10 Laps around Silverlight 5
- Author(s) Michael Crump
- Publisher: SilverlightShow.net (October 11, 2011)
- Paperback: N/A
- eBook: PDF, Word, EPUB, MOBI,
- Language: English
- ISBN-10: N/A
- ISBN-13: N/A
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This ebook collects all 10 parts of SilverlightShow.net article series '10 Laps around Silverlight 5 series'. This resource, authored by Silverlight MVP and Silverlight insider Michael Crump, is a complete guide to all new features of Silverlight 5, with uptodate code samples, demos and valuable references.
About the Authors- Michael Crump is a Microsoft MVP, INETA Community Champion, and an author of several .NET books. He has been seen speaking at a variety of conferences including: CodeStock, DevLink, and TechDays.
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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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