Mastering Net Machine Learning Jamie Dixon Pdf
Download Mastering Net Machine Learning Jamie Dixon Pdf PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Mastering Net Machine Learning Jamie Dixon Pdf book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Mastering .NET Machine Learning
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.
Creativity for 21st Century Skills
Author: Jane Piirto
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-10-23
VERY practical, on target for schools today—good balance of theory with anecdotal connections.” “At first I was worried about the time involved. I discovered when given 5 minutes . . . the time is a continuation to their work in progress. Realizing that creativity does not have to consume large chunks of time is more meaningful than tokens.” “I like the tone of the writing. It feels like there is a conversation going on.” “I like the stories of famous people and how their creativity influenced and changed their lives.” CREATIVITY FOR 21ST CENTURY SKILLS describes what many creative people really do when they create. It focuses on the practical applications of a theoretical approach to creativity training the author has developed. Many suggestions for enhancing creativity focus on ideas that are over 60 years old. This new approach may be helpful for those seeking to develop 21st Century Skills of creativity. Five core attitudes (Naiveté, Risk-taking, Self-Discipline, Tolerance for Ambiguity, and Group Trust), Seven I’s (Inspiration, Intuition, Improvisation, Imagination, Imagery, Incubation, and Insight), and several General Practices—the use of ritual, meditation, solitude, exercise, silence, and a creative attitude to the process of life, with corresponding activities, are described, discussed, and illustrated. A discussion of how to be creative within an educational institution is also included. JANE PIIRTO is Trustees’ Distinguished Professor at Ashland University. Her doctorate is in educational leadership. She has worked with students pre-K to doctoral level as a teacher, administrator, and professor. She has published 11 books, both literary and scholarly, and many scholarly articles in peer-reviewed journals and anthologies, as well as several poetry and creative nonfiction chapbooks. She has won Individual Artist Fellowships from the Ohio Arts Council in both poetry and fiction and is one of the few American writers listed as both a poet and a writer in the Directory of American Poets and Writers. She is a recipient of the Mensa Lifetime Achievement Award, of an honorary Doctor of Humane Letters, was named an Ohio Magazine educator of distinction. In 2010 she was named Distinguished Scholar by the National Association for Gifted Children.
The Alignment Problem: Machine Learning and Human Values
Author: Brian Christian
language: en
Publisher: W. W. Norton & Company
Release Date: 2020-10-06
"If you’re going to read one book on artificial intelligence, this is the one." —Stephen Marche, New York Times A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us—and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole—and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands. The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software. In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story. The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful.