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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.

STEM classroom bundle features four comprehensive STEM products including the force and motion activity set, simple machines activity set, magnets activity set, and sink or float activity set. Children will have the opportunity to explore the wonders of magnetism and engineering, why things sink and float, all six simple machines, and the science of "how things go" with engaging tools, hands-on activities and experiments. Classroom set provides the perfect introduction to building key science, technology, engineering and math skills. All sets include a comprehensive activity guide with reproducible materials. Classroom bundle is designed for children ages 5 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/Dark Gray. Pattern: Animal Print.

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: Striped.

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The minimum description length (MDL) principle is a powerful method of inductive inference, the basis of statistical modeling, pattern recognition, and machine learning. It holds that the best explanation, given a limited set of observed data, is the one that permits the greatest compression of the data. MDL methods are particularly well-suited for dealing with model selection, prediction, and estimation problems in situations where the models under consideration can be arbitrarily complex, and overfitting the data is a serious concern.This extensive, step-by-step introduction to the MDL Principle provides a comprehensive reference (with an emphasis on conceptual issues) that is accessible to graduate students and researchers in statistics, pattern classification, machine learning, and data mining, to philosophers interested in the foundations of statistics, and to researchers in other applied sciences that involve model selection, including biology, econometrics, and experimental psychology. Part I provides a basic introduction to MDL and an overview of the concepts in statistics and information theory needed to understand MDL. Part II treats universal coding, the information-theoretic notion on which MDL is built, and part III gives a formal treatment of MDL theory as a theory of inductive inference based on universal coding. Part IV provides a comprehensive overview of the statistical theory of exponential families with an emphasis on their information-theoretic properties. The text includes a number of summaries, paragraphs offering the reader a "fast track" through the material, and boxes highlighting the most important concepts.

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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.

The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems.Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learningBe able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clusteringLearn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection

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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

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

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Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis.Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike.Learn how machine learning has contributed to the success of modern spam filtersQuickly detect anomalies, including breaches, fraud, and impending system failureConduct malware analysis by extracting useful information from computer binariesUncover attackers within the network by finding patterns inside datasetsExamine how attackers exploit consumer-facing websites and app functionalityTranslate your machine learning algorithms from the lab to productionUnderstand the threat attackers pose to machine learning solutions

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