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With this machines in motion building set, children engineer their own playtime fun with the help of creative, open-ended building activities. Using the included instructions, children can build their own versions of numerous different interactive machines and vehicles, many of which incorporate special parts including a wrecking ball, chains, pulleys, and wheels. Turn out a tank with its own helicopter propeller or craft a complex contraption that spins with the twist of a lever. Children can also set the instructions aside and bring their own designs to life. Thanks to the open-ended designs and interchangeable parts, this set encourages children to discover their inner engineer and hone STEM skills such as critical thinking and trial and error. Interchangeable parts work with other Learning Resources Gears sets (sold separately) to increase the playability of every piece. Building set is designed for children ages 4 and above.

Bring playtime fun and learning adventures into children's room decor with Kid's Playhouse Rugs. These interactive area rugs add color and childlike charm to kids' bedrooms and playrooms. Made of high performance yarns with a non-slip backing making these easy-care rugs the ideal choice for any boy's or girl's room. Color: Blue/Green. Pattern: Animal Print,Border.

Bring playtime fun and learning adventures into children's room decor with Kid's Playhouse Rugs. These interactive area rugs add color and childlike charm to kids' bedrooms and playrooms. Made of high performance yarns with a non-slip backing making these easy-care rugs the ideal choice for any boy's or girl's room. Color: Beige/Green. Pattern: Abstract.

Bring playtime fun and learning adventures into children's room decor with Kid's Playhouse Rugs. These interactive area rugs add color and childlike charm to kids' bedrooms and playrooms. Made of high performance yarns with a non-slip backing making these easy-care rugs the ideal choice for any boy's or girl's room. Color: Green/Charcoal. Pattern: Striped.

Bring playtime fun and learning adventures into children's room decor with Kid's Playhouse Rugs. These interactive area rugs add color and childlike charm to kids' bedrooms and playrooms. Made of high performance yarns with a non-slip backing making these easy-care rugs the ideal choice for any boy's or girl's room. Color: Blue/Dark Gray. Pattern: Animal Print.

This lively and original book offers a provocative critique of the dominant assumptions regarding human action and communication which underlie recent research in machine intelligence. Lucy Suchman argues that the planning model of interaction favoured by the majority of AI researchers does not take sufficient account of the situatedness of most human social behaviour. The problems that can arise as a result are pertinently, and often amusingly, illustrated by the careful analysis of a recorded interaction between novice users and an intelligent machine, whose design has failed to accommodate essential resources of successful human communication. Plans and Situated Actions presents a compelling case for the re-examination of current models underlying interface design. Lucy Suchman's proposals for a fresh characterisation of human-computer interaction which also incorporates recent insights from the social sciences provides a challenge that everyone interested in machine intelligence will seriously need to consider.

Please Read Notes: Brand New, International Softcover Edition, Printed in black and white pages, minor self wear on the cover or pages, Sale restriction may be printed on the book, but Book name, contents, and author are exactly same as Hardcover Edition. Fast delivery through DHL/FedEx express.

The goal of Optimal Transport (OT) is to define geometric tools that are useful to compare probability distributions. Their use dates back to 1781.Recent years have witnessed a new revolution in the spread of OT, thanks to the emergence of approximate solvers that can scale to sizes and dimensions that are relevant to data sciences. Thanks to this newfound scalability, OT is being increasingly used to unlock various problems in imaging sciences (such as color or texture processing), computer vision and graphics (for shape manipulation) or machine learning (for regression, classification and density fitting). This monograph reviews OT with a bias toward numerical methods and their applications in data sciences, and sheds lights on the theoretical properties of OT that make it particularly useful for some of these applications.Computational Optimal Transport presents an overview of the main theoretical insights that support the practical effectiveness of OT before explaining how to turn these insights into fast computational schemes. Written for readers at all levels, the authors provide descriptions of foundational theory at two-levels. Generally accessible to all readers, more advanced readers can read the specially identified more general mathematical expositions of optimal transport tailored for discrete measures. Furthermore, several chapters deal with the interplay between continuous and discrete measures, and are thus targeting a more mathematically-inclined audience.This monograph will be a valuable reference for researchers and students wishing to get a thorough understanding of Computational Optimal Transport, a mathematical gem at the interface of probability, analysis and optimization.

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Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models, and world models.Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative.Discover how variational autoencoders can change facial expressions in photosBuild practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generationCreate recurrent generative models for text generation and learn how to improve the models using attentionUnderstand how generative models can help agents to accomplish tasks within a reinforcement learning settingExplore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

As the Big Data explosion continues at an almost incomprehensible rate, being able to understand and process it becomes even more challenging. With Building Machine Learning Systems with Python, you'll learn everything you need to tackle the modern data deluge - by harnessing the unique capabilities of Python and its extensive range of numerical and scientific libraries, you will be able to create complex algorithms that can 'learn' from data, allowing you to uncover patterns, make predictions, and gain a more in-depth understanding of your data. Featuring a wealth of real-world examples, this book provides gives you with an accessible route into Python machine learning. Learn the Iris dataset, find out how to build complex classifiers, and get to grips with clustering through practical examples that deliver complex ideas with clarity. Dig deeper into machine learning, and discover guidance on classification and regression, with practical machine learning projects outlining effective strategies for sentiment analysis and basket analysis. The book also takes you through the latest in computer vision, demonstrating how image processing can be used for pattern recognition, as well as showing you how to get a clearer picture of your data and trends by using dimensionality reduction. Keep up to speed with one of the most exciting trends to emerge from the world of data science and dig deeper into your data with Python with this unique data science tutorial. Learn how to create machine learning algorithms using the flexibility of Python Get to grips with scikit-learn and other Python scientific libraries that support machine learning projects Employ computer vision using mahotas for image processing that will help you uncover patterns and trends in your data Learn topic modelling and build a topic model for Wikipedia Analyze Twitter data using sentiment analysis Get to grips with classification and regression with real-world examples

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Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes:  A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more.What you'll learn:How to write code for machine learning, adaptive control and estimation using MATLABHow these three areas complement each otherHow these three areas are needed for robust machine learning applicationsHow to use MATLAB graphics and visualization tools for machine learningHow to code real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers, data scientists and students wanting a comprehensive and code cookbook rich in examples on machine learning using MATLAB.

Beginning Apache Spark 3: With Dataframe, Spark Sql, Structured Streaming, and Spark Machine Learning Library 1.74

Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.

Book is in Used-VeryGood condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain very limited notes and highlighting. 1.75

Michalski, Ryszard S., Carbonell, Jaime G., Mitchell, Tom M.

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Practical AI for Healthcare Professionals: Machine Learning with Numpy, Scikit-Learn, and Tensorflow 0.84