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Reinforcement Learning, second edition

Author : Richard S. Sutton
Publisher : MIT Press
ISBN 13 : 0262352702
Total Pages : 549 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Reinforcement Learning, second edition by : Richard S. Sutton

Download or read book Reinforcement Learning, second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

NetLearning

Author : Ferdi Serim
Publisher : O'Reilly Media
ISBN 13 :
Total Pages : 316 pages
Book Rating : 4.F/5 ( download)

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Book Synopsis NetLearning by : Ferdi Serim

Download or read book NetLearning written by Ferdi Serim and published by O'Reilly Media. This book was released on 1996 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, NetAngels (Internet users exploring the Internet's potential for education) share stories to help teachers uncover the benefits of using this medium to its fullest potential in the classroom. The stories take the reader through the use of tools from a teacher's perspective and provide tips on how to effectively integrate the tools and resources into the classroom.

Mastering .NET Machine Learning

Author : Jamie Dixon
Publisher : Packt Publishing Ltd
ISBN 13 : 1785881191
Total Pages : 358 pages
Book Rating : 4.7/5 (858 download)

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Book Synopsis Mastering .NET Machine Learning by : Jamie Dixon

Download or read book Mastering .NET Machine Learning written by Jamie Dixon and published by Packt Publishing Ltd. This book was released on 2016-03-29 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the art of machine learning with .NET and gain insight into real-world applications About This Book Based on .NET framework 4.6.1, includes examples on ASP.NET Core 1.0 Set up your business application to start using machine learning techniques Familiarize the user with some of the more common .NET libraries for machine learning Implement several common machine learning techniques Evaluate, optimize and adjust machine learning models Who This Book Is For This book is targeted at .Net developers who want to build complex machine learning systems. Some basic understanding of data science is required. What You Will Learn Write your own machine learning applications and experiments using the latest .NET framework, including .NET Core 1.0 Set up your business application to start using machine learning. Accurately predict the future using regressions. Discover hidden patterns using decision trees. Acquire, prepare, and combine datasets to drive insights. Optimize business throughput using Bayes Classifier. Discover (more) hidden patterns using KNN and Naive Bayes. Discover (even more) hidden patterns using K-Means and PCA. Use Neural Networks to improve business decision making while using the latest ASP.NET technologies. Explore “Big Data”, distributed computing, and how to deploy machine learning models to IoT devices – making machines self-learning and adapting Along the way, learn about Open Data, Bing maps, and MBrace In Detail .Net is one of the widely used platforms for developing applications. With the meteoric rise of Machine learning, developers are now keen on finding out how can they make their .Net applications smarter. Also, .NET developers are interested into moving into the world of devices and how to apply machine learning techniques to, well, machines. This book is packed with real-world examples to easily use machine learning techniques in your business applications. You will begin with introduction to F# and prepare yourselves for machine learning using .NET framework. You will be writing a simple linear regression model using an example which predicts sales of a product. Forming a base with the regression model, you will start using machine learning libraries available in .NET framework such as Math.NET, Numl.NET and Accord.NET with the help of a sample application. You will then move on to writing multiple linear regressions and logistic regressions. You will learn what is open data and the awesomeness of type providers. Next, you are going to address some of the issues that we have been glossing over so far and take a deep dive into obtaining, cleaning, and organizing our data. You will compare the utility of building a KNN and Naive Bayes model to achieve best possible results. Implementation of Kmeans and PCA using Accord.NET and Numl.NET libraries is covered with the help of an example application. We will then look at many of issues confronting creating real-world machine learning models like overfitting and how to combat them using confusion matrixes, scaling, normalization, and feature selection. You will now enter into the world of Neural Networks and move your line of business application to a hybrid scientific application. After you have covered all the above machine learning models, you will see how to deal with very large datasets using MBrace and how to deploy machine learning models to Internet of Thing (IoT) devices so that the machine can learn and adapt on the fly Style and approach This book will guide you in learning everything about how to tackle the flood of data being encountered these days in your .NET applications with the help of popular machine learning libraries offered by the .NET framework.

Hands-On Machine Learning with ML.NET

Author : Jarred Capellman
Publisher : Packt Publishing Ltd
ISBN 13 : 1789804299
Total Pages : 287 pages
Book Rating : 4.7/5 (898 download)

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Book Synopsis Hands-On Machine Learning with ML.NET by : Jarred Capellman

Download or read book Hands-On Machine Learning with ML.NET written by Jarred Capellman and published by Packt Publishing Ltd. This book was released on 2020-03-27 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core Key FeaturesGet well-versed with the ML.NET framework and its components and APIs using practical examplesLearn how to build, train, and evaluate popular machine learning algorithms with ML.NET offeringsExtend your existing machine learning models by integrating with TensorFlow and other librariesBook Description Machine learning (ML) is widely used in many industries such as science, healthcare, and research and its popularity is only growing. In March 2018, Microsoft introduced ML.NET to help .NET enthusiasts in working with ML. With this book, you’ll explore how to build ML.NET applications with the various ML models available using C# code. The book starts by giving you an overview of ML and the types of ML algorithms used, along with covering what ML.NET is and why you need it to build ML apps. You’ll then explore the ML.NET framework, its components, and APIs. The book will serve as a practical guide to helping you build smart apps using the ML.NET library. You’ll gradually become well versed in how to implement ML algorithms such as regression, classification, and clustering with real-world examples and datasets. Each chapter will cover the practical implementation, showing you how to implement ML within .NET applications. You’ll also learn to integrate TensorFlow in ML.NET applications. Later you’ll discover how to store the regression model housing price prediction result to the database and display the real-time predicted results from the database on your web application using ASP.NET Core Blazor and SignalR. By the end of this book, you’ll have learned how to confidently perform basic to advanced-level machine learning tasks in ML.NET. What you will learnUnderstand the framework, components, and APIs of ML.NET using C#Develop regression models using ML.NET for employee attrition and file classificationEvaluate classification models for sentiment prediction of restaurant reviewsWork with clustering models for file type classificationsUse anomaly detection to find anomalies in both network traffic and login historyWork with ASP.NET Core Blazor to create an ML.NET enabled web applicationIntegrate pre-trained TensorFlow and ONNX models in a WPF ML.NET application for image classification and object detectionWho this book is for If you are a .NET developer who wants to implement machine learning models using ML.NET, then this book is for you. This book will also be beneficial for data scientists and machine learning developers who are looking for effective tools to implement various machine learning algorithms. A basic understanding of C# or .NET is mandatory to grasp the concepts covered in this book effectively.

Machine Learning Projects for .NET Developers

Author : Mathias Brandewinder
Publisher : Apress
ISBN 13 : 1430267666
Total Pages : 290 pages
Book Rating : 4.4/5 (32 download)

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Book Synopsis Machine Learning Projects for .NET Developers by : Mathias Brandewinder

Download or read book Machine Learning Projects for .NET Developers written by Mathias Brandewinder and published by Apress. This book was released on 2015-07-09 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. If you’re new to F#, this book will give you everything you need to get started. If you’re already familiar with F#, this is your chance to put the language into action in an exciting new context. In a series of fascinating projects, you’ll learn how to: Build an optical character recognition (OCR) system from scratch Code a spam filter that learns by example Use F#’s powerful type providers to interface with external resources (in this case, data analysis tools from the R programming language) Transform your data into informative features, and use them to make accurate predictions Find patterns in data when you don’t know what you’re looking for Predict numerical values using regression models Implement an intelligent game that learns how to play from experience Along the way, you’ll learn fundamental ideas that can be applied in all kinds of real-world contexts and industries, from advertising to finance, medicine, and scientific research. While some machine learning algorithms use fairly advanced mathematics, this book focuses on simple but effective approaches. If you enjoy hacking code and data, this book is for you.

Deep Learning

Author : Ian Goodfellow
Publisher : MIT Press
ISBN 13 : 0262035618
Total Pages : 801 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Deep Learning by : Ian Goodfellow

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-18 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Learning Visual Basic .NET

Author : Jesse Liberty
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 0596552173
Total Pages : 323 pages
Book Rating : 4.5/5 (965 download)

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Book Synopsis Learning Visual Basic .NET by : Jesse Liberty

Download or read book Learning Visual Basic .NET written by Jesse Liberty and published by "O'Reilly Media, Inc.". This book was released on 2002-10-25 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most Visual Basic .NET books are written for experienced object-oriented programmers, but many programmers jumping on the .NET bandwagon are coming from non-object-oriented languages, such as Visual Basic 6.0 or from script programming, such as JavaScript. These programmers, and those who are adopting VB.NET as their first programming language, have been out of luck when it comes to finding a high-quality introduction to the language that helps them get started.That's why Jesse Liberty, author of the best-selling books Programming C# and Programming ASP.NET, has written an entry-level guide to Visual Basic .NET. Written in a warm and friendly manner, this book assumes no prior programming experience, and provides an easy introduction to Microsoft's most popular .NET language.Learning Visual Basic .NET is a complete introduction to VB.NET and object-oriented programming. This book will help you build a solid foundation in .NET, and show how to apply your skills by using hundreds of examples to help you become productive quickly. Learning Visual Basic .NET introduces fundamentals like Visual Studio .NET, a tool set for building Windows and Web applications. You'll learn about the syntax and structure of the Visual Basic .NET language, including operators, classes and interfaces, structs, arrays, and strings. Liberty then demonstrates how to develop various kinds of applications--including those that work with databases--and web services.By the time you've finished Learning Visual Basic .NET, you'll be ready to move on to a more advanced programming guide that will help you create large-scale web and Windows applications.Whether you have a little object-oriented programming experience or you are new to programming altogether, Visual Basic .NET will set you firmly on your way to mastering the essentials of the VB.NET language.

Learning Visual Basic 2008 with .Net Framework 3.5

Author : Cadcim
Publisher : Pearson Education India
ISBN 13 : 9788131722862
Total Pages : 446 pages
Book Rating : 4.7/5 (228 download)

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Book Synopsis Learning Visual Basic 2008 with .Net Framework 3.5 by : Cadcim

Download or read book Learning Visual Basic 2008 with .Net Framework 3.5 written by Cadcim and published by Pearson Education India. This book was released on with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Learning ASP.NET 2.0 with AJAX

Author : Jesse Liberty
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 0596551606
Total Pages : 523 pages
Book Rating : 4.5/5 (965 download)

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Book Synopsis Learning ASP.NET 2.0 with AJAX by : Jesse Liberty

Download or read book Learning ASP.NET 2.0 with AJAX written by Jesse Liberty and published by "O'Reilly Media, Inc.". This book was released on 2007-09-27 with total page 523 pages. Available in PDF, EPUB and Kindle. Book excerpt: With this book, web developers can build engaging and interactive sites and applications using Microsoft's latest web development tools -- ASP.NET 2.0 and the new ASP.NET AJAX framework. You learn to create applications that have all the great tricks you see on popular commercial web sites, such as order forms and the ability to interact with a database. And you can build pages that display information interactively without a page refresh. This straightforward tutorial explains how. Learning ASP.NET 2.0 with AJAX helps you master the concepts and techniques of Microsoft's tools with plenty of annotated examples, review quizzes, web construction exercises and chapter summaries, so you can practice new skills and test your understanding as you go. With it, you'll learn to: Master the fundamental skills of ASP.NET 2.0 to build professional quality web applications Integrate new Ajax tools and CSS with ASP.NET 2.0 for flashier and more interactive sites Build applications with minimal coding using Visual Studio or its free counterpart, Visual Web Developer Connect your site with a database so that users can retrieve, interact and save data Debug your application, deal with unexpected problems, and protect your site from malicious users Use the community-maintained ASP.NET AJAX Control Toolkit to extend the controls that come with ASP.NET AJAX Use personalization tools to give your site a customized look for each user Ideal for beginning web developers, or those who are new to ASP.NET, this book gets you involved with your own learning through hands-on lessons that are clear and to the point. You get the chance to try out new techniques on the spot. Want to join the world of modern web development? This book will get you started.

Educating the Net Generation

Author : Diana Oblinger
Publisher : Educause
ISBN 13 : 9780967285320
Total Pages : pages
Book Rating : 4.2/5 (853 download)

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Book Synopsis Educating the Net Generation by : Diana Oblinger

Download or read book Educating the Net Generation written by Diana Oblinger and published by Educause. This book was released on 2005-01-01 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This e-book offers an insightful look into the way today's students think about and use technology in their academic and social lives. It will help institutional leaders help their students to become more successful and satisfied.

Deep Learning with C#, .Net and Kelp.Net

Author : Cole Matt R.
Publisher : BPB Publications
ISBN 13 : 9389423740
Total Pages : 388 pages
Book Rating : 4.3/5 (894 download)

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Book Synopsis Deep Learning with C#, .Net and Kelp.Net by : Cole Matt R.

Download or read book Deep Learning with C#, .Net and Kelp.Net written by Cole Matt R. and published by BPB Publications. This book was released on 2019-09-20 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get hands on with Kelp.Net, Microsoft's latest Deep Learning frameworkKey features Deep Learning Basics The ultimate Kelp.Net reference guide Develop state of the art deep learning applications C# deep learning code Develop advanced deep learning models with minimal code Develop your own advanced deep learning models Loading and Saving Deep Learning Models Comprehensive Kelp.Net reference Sample Deep Learning Models and Tests penCL Reference Easily add deep learning to your applications Many sample models and tests Intuitive and user friendly Description Deep Learning with Kelp.Net is the ultimate reference for C# .Net developers who are passionate about deep learning. Readers will learn all the skills necessary to develop powerful, scalable and flexible deep learning models from a fluid and easy to use API. Upon completing the book the reader will have all the tools necessary to add powerful deep learning capabilities to their new or existing applications.What will you learn In-depth knowledge of Kelp.Net How to develop deep learning models C# deep learning programming Open-Computing Language (OpenCL) Loading and saving deep learning models How to develop and use activation functions How to test deep learning modelsWho this book is for This book targets C# .Net developers who are passionate about deep learning yet want to do so from an easy and intuitive API.Table of contents1. Introduction2. ML/DL Terms and Concepts3. Deep Instrumentation4. Kelp.Net Reference5. Loading and Saving Models6. Model Testing and Training7. Sample Deep Learning Tests8. Creating Your Own Deep Learning Tests9. Appendix A: Evaluation Metrics10. Appendix B: OpenCL About the authorMatt R. Cole is a seasoned developer and published author with over 30 years' experience in Microsoft Windows, C, C++, C# and .Net. Matt is the owner of Evolved AI Solutions, a premier provider of advanced Machine Learning/Bio-AI technologies. Matt developed the first enterprise grade MicroService framework written completely in C# and .Net, which is used in production by a major hedge fund in NYC. Matt also developed the first Bio Artificial Intelligence framework which completely integrates mirror and canonical neurons. He continues to push the limits of Machine Learning, Biological Artificial Intelligence, Deep Learning and MicroServices. In his spare time Matt loves to continue his education and contribute to open source efforts such as Kelp.Net. His Website: www.evolvedaisolutions.comHis LinkedIn Profile: https://www.linkedin.com/in/evolvedai/His Blog: https://evolvedaisolutions.com/blog.html

Learning ASP.NET 3.5

Author : Jesse Liberty
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 0596551800
Total Pages : 611 pages
Book Rating : 4.5/5 (965 download)

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Book Synopsis Learning ASP.NET 3.5 by : Jesse Liberty

Download or read book Learning ASP.NET 3.5 written by Jesse Liberty and published by "O'Reilly Media, Inc.". This book was released on 2008-07-25 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: With this book, you will learn how to create engaging and interactive web applications using the latest version of the world's most popular web development platform: ASP.NET with AJAX, built on the productivity-enhancing features of Visual Studio 2008. All you need to get started is a basic knowledge of HTML and a desire to produce professional quality websites. Learning ASP.NET 3.5 introduces new skills in each new chapter and offers fully annotated and fully functional examples that you can put to work immediately. Each chapter adds detailed summaries, practice questions to ensure comprehension, and exercises so you can apply what you've learned to new situations. Written by the bestselling author team of Jesse Liberty, Dan Hurwitz, and Brian MacDonald, Learning ASP.NET 3.5 offers complete, up-to-date coverage of ASP.NET 3.5 and AJAX. The book includes: Chapters that are designed as a series of tutorials on different aspects of web development Examples in each chapter that illustrate how a new concept works. Different chapters feature either a single running example with several stages, or a series of smaller examples A single large example in the final chapter offers that uses everything the reader has learned VB, JavaScript, and SQL Cheat Sheet sidebars to help readers with no little or no background with those topics AJAX-style fully integrated into ASP.NET programming -- the way it should be taught and used If you want to get up to speed with the world's most popular web development technology, Learning ASP.NET 3.5 is the best resource for the job.

Ultimate Machine Learning with ML.NET:

Author : Kalicharan Mahasivabhattu
Publisher : Orange Education Pvt Ltd
ISBN 13 : 8197256373
Total Pages : 244 pages
Book Rating : 4.1/5 (972 download)

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Book Synopsis Ultimate Machine Learning with ML.NET: by : Kalicharan Mahasivabhattu

Download or read book Ultimate Machine Learning with ML.NET: written by Kalicharan Mahasivabhattu and published by Orange Education Pvt Ltd. This book was released on 2024-06-30 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: TAGLINE “Empower Your .NET Journey with Machine Learning” KEY FEATURES ● Step-by-step guidance to help you navigate through various machine learning tasks and techniques with ML.NET. ● Explore all aspects of ML.NET, from installation and configuration to model deployment. ● Engage in practical exercises and real-world projects to solidify your understanding. ● Learn how to optimize, tune, and interpret your ML.NET models for maximum accuracy and performance. DESCRIPTION Dive into the world of machine learning for data-driven insights and seamless integration in .NET applications with the Ultimate Machine Learning with ML.NET. The book begins with foundations of ML.NET and seamlessly transitions into practical guidance on installing and configuring it using essential tools like Model Builder and the command-line interface. Next, it dives into the heart of machine learning tasks using ML.NET, exploring classification, regression, and clustering with its versatile functionalities. It will delve deep into the process of selecting and fine-tuning algorithms to achieve optimal performance and accuracy. You will gain valuable insights into inspecting and interpreting ML.NET models, ensuring they meet your expectations and deliver reliable results. It will teach you efficient methods for saving, loading, and sharing your models across projects, facilitating seamless collaboration and reuse. The final section of the book covers advanced techniques for optimizing model accuracy and refining performance. You will be able to deploy your ML.NET models using Azure Functions and Web API, empowering you to integrate machine learning solutions seamlessly into real-world applications. WHAT WILL YOU LEARN ● Understand the basics of ML.NET and its capabilities in the machine learning landscape. ● Gain practical experience with the ML.NET Model Builder and command-line interface (CLI) to efficiently create models. ● Understand how to choose the most suitable algorithms and fine-tune them for optimal performance within ML.NET. ● Acquire knowledge on saving and loading ML.NET models, making them reusable and shareable across different projects. ● Delve into advanced strategies for enhancing the accuracy of your ML.NET models. ● Discover how to deploy ML.NET models using Azure Functions and Web API, enabling real-world application integration and scalability. WHO IS THIS BOOK FOR? This book is tailored for professionals and enthusiasts such as software developers, data scientists, and machine learning engineers who want to build and deploy machine learning models within the .NET ecosystem. IT professionals and technical leads overseeing machine learning projects in a .NET environment will also find this book valuable. Readers should have basic programming knowledge and a foundational understanding of machine learning concepts. TABLE OF CONTENTS 1. Introduction to ML.NET 2. Installing and Configuring ML.NET 3. ML.NET Model Builder and CLI 4. Collecting and Preparing Data for ML.NET 5. Machine Learning Tasks in ML.NET 6. Choosing and Tuning Machine Learning Algorithms in ML.NET 7. Inspecting and Interpreting ML.NET Models 8. Saving and Loading Models in ML.Net 9. Optimizing ML.NET Models for Accuracy 10. Deploying ML.NET Models with Azure Functions and Web API Index

Deep Learning with C#, .Net and Kelp.Net

Author : Matt R. Cole
Publisher : BPB Publications
ISBN 13 : 9388511018
Total Pages : 414 pages
Book Rating : 4.3/5 (885 download)

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Book Synopsis Deep Learning with C#, .Net and Kelp.Net by : Matt R. Cole

Download or read book Deep Learning with C#, .Net and Kelp.Net written by Matt R. Cole and published by BPB Publications. This book was released on 2019-05-14 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get hands on with Kelp.Net , Microsoft’s latest Deep Learning framework Key Features Deep Learning Basics The ultimate Kelp.Net reference guide Develop state of the art deep learning applications C# Deep Learning code Develop advanced deep learning models with minimal code Develop your own advanced Deep Learning models Loading and Saving Deep Learning Models Comprehensive Kelp.Net reference Sample Deep Learning Models and Tests OpenCL Reference Easily add deep learning to your applications Many sample models and tests Intuitive and user friendly Description Deep Learning with Kelp.Net is the ultimate reference for C# .Net developers who are passionate about deep learning. Readers will learn all the skills necessary to develop powerful, scalable and flexible deep learning models from a fluid and easy to use API. Upon completing the book the reader will have all the tools necessary to add powerful deep learning capabilities to their new or existing applications. What you will learn In-depth knowledge of Kelp.Net How to develop Deep Learning models C# Deep Learning programming Open-Computing Language (OpenCL) Loading and saving Deep Learning models How to develop and use activation functions How to test Deep Learning models Who This Book is For This book targets C# .Net developers who are passionate about deep learning yet want to do so from an easy and intuitive API. Table of Contents Introduction ML/DL Terms and Concepts Deep Instrumentation Kelp.Net Reference Loading and Saving Models Model Testing and Training Sample Deep Learning Tests Creating Your Own Deep Learning Tests Appendix A: Evaluation Metrics Appendix B: OpenCL About the Author Matt R. Cole is a seasoned developer and published author with over 30 years’ experience in Microsoft Windows, C, C++, C# and .Net. He is the owner of Evolved AI Solutions, a premier provider of advanced Machine Learning/Bio-AI technologies. He developed the first enterprise grade MicroService framework written completely in C# and .Net, which is used in production by a major hedge fund in NYC. He also developed the first Bio Artificial Intelligence framework which completely integrates mirror and canonical neurons. He continues to push the limits of Machine Learning, Biological Artificial Intelligence, Deep Learning and MicroServices. In his spare time Matt loves to continue his education and contribute to open source efforts such as Kelp.Net. His Website: www.evolvedaisolutions.com His LinkedIn Profile: www.linkedin.com/in/evolvedai/ His Blog: www.evolvedaisolutions.com/blog.html

Teaching, Learning and the Net Generation: Concepts and Tools for Reaching Digital Learners

Author : Ferris, Sharmila Pixy
Publisher : IGI Global
ISBN 13 : 1613503482
Total Pages : 488 pages
Book Rating : 4.6/5 (135 download)

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Book Synopsis Teaching, Learning and the Net Generation: Concepts and Tools for Reaching Digital Learners by : Ferris, Sharmila Pixy

Download or read book Teaching, Learning and the Net Generation: Concepts and Tools for Reaching Digital Learners written by Ferris, Sharmila Pixy and published by IGI Global. This book was released on 2011-11-30 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although a growing body of research demonstrates the need for education to adapt to the needs of the Net Generation, research also shows that traditional teaching methods continue to dominate the classroom. To stay effective, higher education must adapt to the needs of this unique generation of digital natives who grew up with computer technologies and social media. Teaching, Learning and the Net Generation: Concepts and Tools for Reaching Digital Learners provides pedagogical resources for understanding digital learners, and effectively teaching and learning with today’s generation of digital natives. This book creates a much-needed resource that moves beyond traditional disciplinary and geographical boundaries, bridges theories and practice, and addresses emerging issues in technology and pedagogy.

Learning .NET High-performance Programming

Author : Antonio Esposito
Publisher : Packt Publishing Ltd
ISBN 13 : 1785282638
Total Pages : 305 pages
Book Rating : 4.7/5 (852 download)

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Book Synopsis Learning .NET High-performance Programming by : Antonio Esposito

Download or read book Learning .NET High-performance Programming written by Antonio Esposito and published by Packt Publishing Ltd. This book was released on 2015-06-30 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book will help you understand what "programming for performance" means, and use effective coding patterns and techniques to optimize your .NET applications. You will begin by understanding what "high performance coding" means, and the different performance concerns. You will see how CLR works and get an understanding of concepts such as memory management, garbage collection, and thread life cycles. You will proceed to learn about the theoretical and practical concepts of PLINQ programming. You will also see what Big Data is, and how to architect a Big Data solution to manipulate large datasets. Finally, you will learn how to launch and analyze a profile session and execute tests against a code block or application for performance analysis. By the end of this book, you will have a complete understanding of efficient programming using high-performance techniques, and will able to write highly optimized applications.

Graph Representation Learning

Author : William L. William L. Hamilton
Publisher : Springer Nature
ISBN 13 : 3031015886
Total Pages : 141 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Graph Representation Learning by : William L. William L. Hamilton

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.