David bellot (30 résultats)

Etat : Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,600grams, ISBN:9781784392055.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New.

Digital. Etat : New. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in RAbout This Book. Predict and use a probabilistic graphical models (PGM) as an expert system. Comprehend how your computer can learn Bayesian modeling to solve real-world problems. Know how to prepare data and feed the models by using the appropriate algorithms from the appropriate R packageWho This Book Is ForThis book is for anyone who has to deal with lots of data and draw conclusions from it, especially when the data is noisy or uncertain. Data scientists, machine learning enthusiasts, engineers, and those who curious about the latest advances in machine learning will find PGM interesting.What You Will Learn. Understand the concepts of PGM and which type of PGM to use for which problem. Tune the model's parameters and explore new models automatically. Understand the basic principles of Bayesian models, from simple to advanced. Transform the old linear regression model into a powerful probabilistic model. Use standard industry models but with the power of PGM. Understand the advanced models used throughout today's industry. See how to compute posterior distribution with exact and approximate inference algorithmsIn DetailProbabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graph-based representation. Two branches of graphical representations of distributions are commonly used, namely Bayesian networks and Markov networks. R has many packages to implement graphical models.We'll start by showing you how to transform a classical statistical model into a modern PGM and then look at how to do exact inference in graphical models. Proceeding, we'll introduce you to many modern R packages that will help you to perform inference on the models. We will then run a Bayesian linear regression and you'll see the advantage of going probabilistic when you want to do prediction.Next, you'll master using R packages and implementing its techniques. Finally, you'll be presented with machine learning applications that have a direct impact in many fields. Here, we'll cover clustering and the discovery of hidden information in big data, as well as two important methods, PCA and ICA, to reduce the size of big problems.Style and approach This book gives you a detailed and step-by-step explanation of each mathematical concept, which will help you build and analyze your own machine learning models and apply them to real-world problems. The mathematics is kept simple and each formula is explained thoroughly.…

Digital. Etat : New. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in RAbout This Book. Predict and use a probabilistic graphical models (PGM) as an expert system. Comprehend how your computer can learn Bayesian modeling to solve real-world problems. Know how to prepare data and feed the models by using the appropriate algorithms from the appropriate R packageWho This Book Is ForThis book is for anyone who has to deal with lots of data and draw conclusions from it, especially when the data is noisy or uncertain. Data scientists, machine learning enthusiasts, engineers, and those who curious about the latest advances in machine learning will find PGM interesting.What You Will Learn. Understand the concepts of PGM and which type of PGM to use for which problem. Tune the model's parameters and explore new models automatically. Understand the basic principles of Bayesian models, from simple to advanced. Transform the old linear regression model into a powerful probabilistic model. Use standard industry models but with the power of PGM. Understand the advanced models used throughout today's industry. See how to compute posterior distribution with exact and approximate inference algorithmsIn DetailProbabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graph-based representation. Two branches of graphical representations of distributions are commonly used, namely Bayesian networks and Markov networks. R has many packages to implement graphical models.We'll start by showing you how to transform a classical statistical model into a modern PGM and then look at how to do exact inference in graphical models. Proceeding, we'll introduce you to many modern R packages that will help you to perform inference on the models. We will then run a Bayesian linear regression and you'll see the advantage of going probabilistic when you want to do prediction.Next, you'll master using R packages and implementing its techniques. Finally, you'll be presented with machine learning applications that have a direct impact in many fields. Here, we'll cover clustering and the discovery of hidden information in big data, as well as two important methods, PCA and ICA, to reduce the size of big problems.Style and approach This book gives you a detailed and step-by-step explanation of each mathematical concept, which will help you build and analyze your own machine learning models and apply them to real-world problems. The mathematics is kept simple and each formula is explained thoroughly.…

Etat : New.

Etat : New. In English.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : As New. Unread book in perfect condition.

Digital. Etat : New. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in RAbout This Book. Predict and use a probabilistic graphical models (PGM) as an expert system. Comprehend how your computer can learn Bayesian modeling to solve real-world problems. Know how to prepare data and feed the models by using the appropriate algorithms from the appropriate R packageWho This Book Is ForThis book is for anyone who has to deal with lots of data and draw conclusions from it, especially when the data is noisy or uncertain. Data scientists, machine learning enthusiasts, engineers, and those who curious about the latest advances in machine learning will find PGM interesting.What You Will Learn. Understand the concepts of PGM and which type of PGM to use for which problem. Tune the model's parameters and explore new models automatically. Understand the basic principles of Bayesian models, from simple to advanced. Transform the old linear regression model into a powerful probabilistic model. Use standard industry models but with the power of PGM. Understand the advanced models used throughout today's industry. See how to compute posterior distribution with exact and approximate inference algorithmsIn DetailProbabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graph-based representation. Two branches of graphical representations of distributions are commonly used, namely Bayesian networks and Markov networks. R has many packages to implement graphical models.We'll start by showing you how to transform a classical statistical model into a modern PGM and then look at how to do exact inference in graphical models. Proceeding, we'll introduce you to many modern R packages that will help you to perform inference on the models. We will then run a Bayesian linear regression and you'll see the advantage of going probabilistic when you want to do prediction.Next, you'll master using R packages and implementing its techniques. Finally, you'll be presented with machine learning applications that have a direct impact in many fields. Here, we'll cover clustering and the discovery of hidden information in big data, as well as two important methods, PCA and ICA, to reduce the size of big problems.Style and approach This book gives you a detailed and step-by-step explanation of each mathematical concept, which will help you build and analyze your own machine learning models and apply them to real-world problems. The mathematics is kept simple and each formula is explained thoroughly.…

Etat : New.

Etat : New. In English.

Digital. Etat : New. Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in RAbout This Book. Predict and use a probabilistic graphical models (PGM) as an expert system. Comprehend how your computer can learn Bayesian modeling to solve real-world problems. Know how to prepare data and feed the models by using the appropriate algorithms from the appropriate R packageWho This Book Is ForThis book is for anyone who has to deal with lots of data and draw conclusions from it, especially when the data is noisy or uncertain. Data scientists, machine learning enthusiasts, engineers, and those who curious about the latest advances in machine learning will find PGM interesting.What You Will Learn. Understand the concepts of PGM and which type of PGM to use for which problem. Tune the model's parameters and explore new models automatically. Understand the basic principles of Bayesian models, from simple to advanced. Transform the old linear regression model into a powerful probabilistic model. Use standard industry models but with the power of PGM. Understand the advanced models used throughout today's industry. See how to compute posterior distribution with exact and approximate inference algorithmsIn DetailProbabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graph-based representation. Two branches of graphical representations of distributions are commonly used, namely Bayesian networks and Markov networks. R has many packages to implement graphical models.We'll start by showing you how to transform a classical statistical model into a modern PGM and then look at how to do exact inference in graphical models. Proceeding, we'll introduce you to many modern R packages that will help you to perform inference on the models. We will then run a Bayesian linear regression and you'll see the advantage of going probabilistic when you want to do prediction.Next, you'll master using R packages and implementing its techniques. Finally, you'll be presented with machine learning applications that have a direct impact in many fields. Here, we'll cover clustering and the discovery of hidden information in big data, as well as two important methods, PCA and ICA, to reduce the size of big problems.Style and approach This book gives you a detailed and step-by-step explanation of each mathematical concept, which will help you build and analyze your own machine learning models and apply them to real-world problems. The mathematics is kept simple and each formula is explained thoroughly.…

Etat : As New. Unread book in perfect condition.

Etat : New.

paperback. Etat : New. Paperback. Pub Date: 2018-01-01 Pages: 185 Language: Chinese Publisher: people's posts and telecommunications press probability graph model combines the knowledge of probability theory and graph theory. provides a simple visual method of probability model. in areas such as artificial intelligence. machine learning and computer vision has a broad application prospect.This book is designed to help readers learn how to use almost.…

PF. Etat : New. This item is printed on demand. Brand new book, sourced directly from publisher. Dispatch time is 6-7 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering.

Taschenbuch. Etat : Neu. Learning Probabilistic Graphical Models in R | David Bellot | Taschenbuch | Kartoniert / Broschiert | Englisch | 2016 | Packt Publishing | EAN 9781784392055 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …

Etat : New. Print on Demand pp. 152 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Etat : New. Print on Demand pp. 152.

Etat : New. PRINT ON DEMAND pp. 152.