Learning Visual F#

Learn F# as a general-purpose programming language in a number of business oriented scenarios whilst making use of the .NET Framework and the Community Edition of Visual Studio 2015.

Machine Learning Projects for .NET Developers shows you how to build smarter .NET applications that learn from data, using simple algorithms and techniques that can be applied to a wide range of real-world problems. You’ll code each project in the familiar setting of Visual Studio, while the machine learning logic uses F#, a language ideally suited to machine learning applications in .NET.

This is a book on the F# programming language.
On the surface of things, that is an intuitively obvious statement, given the title of this book. However, despite the apparent redundancy in saying it aloud, the sentence above elegantly describes what this book is about: The authors are not attempting to teach developers how to accomplish tasks from other languages in this one, nor are they attempting to evangelize the language or its feature set or its use "over" other languages. They assume that you are considering this book because you have an interest in learning the F# language: its syntax, its semantics, its pros and cons, and its use in concert with other parts of the .NET ecosystem.

The 121 revised full papers and 52 short papers presented together with the abstracts of 3 invited papers and those of the keynote speeches were carefully reviewed and selected from a total of 467 submissions. The papers cover a wide range of topics, including e-learning platforms and tools, learning resource management, practice and experience sharing, e-learning standards, mobile learning, education and remote classrooms, effectiveness of VR for education, life long learning, collaborative environments, remote group simulations, collaborative learning, virtual reality in education, game design and development, game engine development, game AI and artificial life, game physics, game rendering, virtual characters/agents, online/mobile game/video game, storytelling and game narrative, affective interaction in games, digital museum, digital heritage, animation techniques, augmented reality, and mixed reality.

Explore Data Science & Machine Learning topics with simple, step-by-step demos and user-friendly Excel models (NO code!)

Learn from basics of Semantic Segmentation using Deep Learning to Implement, Train and Test YOUR own Models in PyTorch

The 170 revised full papers presented were carefully reviewed and selected from 557 submissions. The papers are organized in topical sections on learning and information processing, data mining, retrieval and management, bioinformatics and bio-inspired models, agents and hybrid systems, financial engineering, as well as a special session on nature-inspired date technologies.