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Castello di Nymphenburg
In Stock. Add To Cart. Be the first to review this item.Il palazzo, al centro di un vasto parco alla franceseera la residenza estiva dei re di Baviera. Il progetto del palazzo venne commissionato dalla coppia di principi elettori Ferdinando Maria di Baviera ed Enrichetta Adelaide di Savoia all'architetto italiano Agostino Barelli nelin seguito alla nascita del loro figlio Massimiliano Emanuele. Il padiglione centrale fu completato nel A partire dalMassimiliano Emanuele, l'erede di Baviera, condusse una sistematica estensione del palazzo.
Furono aggiunti due padiglioni, nell'ala sud e nell'ala nord del palazzo di Barelli, dagli architetti Enrico Zuccalli e Giovanni Antonio Viscardi. In seguito, la parte sud del palazzo fu ulteriormente estesa a formare le stalle di corte. Per bilanciare, fu aggiunta l'Orangerie nella parte nord. Per lungo tempo, il palazzo fu la residenza estiva preferita dei sovrani di Baviera.
Le facciate barocche comprendono una larghezza complessiva di circa metri. L'ex "sala da pranzo piccola" nel padiglione sud oggi ospita la Galleria delle Bellezze voluta dal re Ludovico I. Questo padiglione ospita anche la stanza in cui nacque re Ludovico II di Baviera. Le scuderie di corte contengono un museo di carrozze antiche Marstallmuseum. Ebbero anche un ruolo negli eventi storici - una di queste carrozze per esempio fu utilizzata per l'incoronazione dell'imperatore Carlo VII nel Tra le attrazioni principali del museo ci sono le magnifiche carrozze e slitte del re Ludovico II.
Il primo piano delle ex scuderie di corte ospita una collezione di porcellane di Nymphenburgla fabbrica situata nel complesso di palazzo fondata da Massimiliano III Giuseppe ; qui sono conservate molte opere di Francesco Antonio Bustelli di Intragna.
A questi si deve anche il Giardino inglese in tedesco: Englischer Garten di Monaco. Ha conservato gli elementi principali del giardino barocco come il grande parterre. Due laghi sono situati su entrambi i lati del canale. Un passaggio nei pressi del vecchio arboreto nel nord del gran parterre conduce al grande giardino botanico di Monaco di Baviera.
Il palazzo e il suo parco sono stati set per le riprese del film di Alain Resnais del L'anno scorso a Marienbad. I percorsi per il dressagetra gli sport equestri delle olimpiadi delfurono creati nel parco di Nymphenburg. Altri progetti. Da Wikipedia, l'enciclopedia libera. Questa voce o sezione sull'argomento castelli non cita le fonti necessarie o quelle presenti sono insufficienti.
Puoi migliorare questa voce aggiungendo citazioni da fonti attendibili secondo le linee guida sull'uso delle fonti. Segui i suggerimenti del progetto di riferimento. Altri progetti Wikimedia Commons. Portale Architettura. Portale Germania.If you try to delete a batch topic distribution a second time, or a batch topic distribution that does not exist, you will receive a "404 not found" response. However, if you try to delete a batch topic distribution that is being used at the moment, then BigML.
To list all the batch topic distributions, you can use the batchtopicdistribution base URL. By default, only the 20 most recent batch topic distributions will be returned. You can get your list of batch topic distributions directly in your browser using your own username and API key with the following links.
You can also paginate, filter, and order your batch topic distributions.
Evaluations Last Updated: Monday, 2017-10-30 10:31 An evaluation provides an easy way to measure the performance of a predictive model. The type of an evaluation can vary.
It can be timeseries type if it is created using a time series. The performance measures provided by BigML will vary depending on the type of evaluation. You can also list all of your evaluations.Json diff
All the fields in the dataset Specifies the fields in the dataset to be considered to create the evaluation. Specifies the type of ordering followed to pick the instances of the dataset to evaluate the model or ensemble. There are three different types that you can specify: 0 Deterministic 1 Linear 2 Random For more information, see the Section on Shuffling for more details. The range of successive instances to evaluate the model.
Example: "MySample" tags optional Array of Strings A list of strings that help classify and index your evaluation. Note that their use is deprecated, and maintained only for backwards compatibility. For example, to create a new evaluation named "my evaluation" using the first 50 instances in the dataset.
Once an evaluation has been successfully created it will have the following properties. That is, each measure is computed with respect to each class, then the computed values are averaged to get the average measure.All players can easily participate and those who play more regularly can receive progressively greater rewards.
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If you are interested in such comparisons, you can explore specific frequencies in the breakdowns tables, trying different orders of independent variables. However, in order to subject such differences to statistical tests, you should use crosstabulations and frequency tables, Log-Linear Analysis, or Correspondence Analysis (for more advanced analyses on multi-way frequency tables).
Graphs can often identify effects (both expected and unexpected) in the data more quickly and sometimes "better" than any other data analysis method. Categorized graphs allow you to plot the means, distributions, correlations, etc.
The graph below shows a categorized histogram which enables you to quickly evaluate and visualize the shape of the data for each group (group1-female, group2-female, etc. The categorized scatterplot (in the graph below) shows the differences between patterns of correlations between dependent variables across the groups.
Additionally, if the software has a brushing facility which supports animated brushing, you can select (i. Frequency or one-way tables represent the simplest method for analyzing categorical (nominal) data (refer to Elementary Concepts).
They are often used as one of the exploratory procedures to review how different categories of values are distributed in the sample. In practically every research project, a first "look" at the data usually includes frequency tables. For example, in survey research, frequency tables can show the number of males and females who participated in the survey, the number of respondents from particular ethnic and racial backgrounds, and so on. Responses on some labeled attitude measurement scales (e.
Customarily, if a data set includes any categorical data, then one of the first steps in the data analysis is to compute a frequency table for those categorical variables. Purpose and Arrangement of Table.
Crosstabulation is a combination of two (or more) frequency tables arranged such that each cell in the resulting table represents a unique combination of specific values of crosstabulated variables.
Thus, crosstabulation allows us to examine frequencies of observations that belong to specific categories on more than one variable. By examining these frequencies, we can identify relations between crosstabulated variables.
Only categorical (nominal) variables or variables with a relatively small number of different meaningful values should be crosstabulated. Note that in the cases where we do want to include a continuous variable in a crosstabulation (e.
The simplest form of crosstabulation is the 2 by 2 table where two variables are "crossed," and each variable has only two distinct values.
MALE FEMALE FEMALE FEMALE MALE. A B B A B. The resulting crosstabulation could look as follows. In general, this table shows us that more females than males chose the soda pop brand A, and that more males than females chose soda B. Thus, gender and preference for a particular brand of soda may be related (later we will see how this relationship can be measured). The values in the margins of the table are simply one-way (frequency) tables for all values in the table.
They are important in that they help us to evaluate the arrangement of frequencies in individual columns or rows. Thus, the differences between the distributions of frequencies in individual rows (or columns) and in the respective margins informs us about the relationship between the crosstabulated variables.
Column, Row, and Total Percentages. The example in the previous paragraph demonstrates that in order to evaluate relationships between crosstabulated variables, we need to compare the proportions of marginal and individual column or row frequencies.
Such comparisons are easiest to perform when the frequencies are presented as percentages.Lou holds an MBA in finance and marketing and a B. He lives in Manhattan where he indulges his passion for bike riding, travel, fine wine and Formula One motor-racing. In May 2017, Lindsay was named Chief Transformation Officer for WPP and GroupM. The newly created position sees Lindsay lead change initiatives across GroupM, its media agencies and specialists, and working closely with leaders across WPP to deliver the best possible communications for clients.
Lindsay previously served as the Global CEO of Maxus, a position held since October 2014. She joined Maxus in October 2009 as UK CEO and in August 2012, Lindsay took on an additional role as Global Chief Strategy Officer for Maxus. Lindsay now sits on the Chime LTD board as a non-executive director, representing WPP. Geoff Ramsey is on the cutting edge of consumer, marketing and media trends in a digital world. Through his dynamic and high-energy keynotes, Ramsey consistently wows audiences by weaving together market data and a powerful narrative explaining the critical implications for marketers.
As chief innovation officer and co-founder of eMarketer, Ramsey has rich insights and a big-picture perspective into all facets of digital and its impact on marketing, business and society. Ramsey has also delivered custom presentations for Fortune 100 corporations including Facebook, Google, Yahoo, Coca-Cola, The Home Depot and American Express. At UWINCorp, Sandra is working on an asset registry services and trading marketplace blockchain project focusing on physical commodity markets. She brings over 15 years of global capital markets, derivatives and market infrastructure experience.
She is the former Executive Director, Head of Digitization at CME Group.
She is also a former Treasurer and founding board member of the Enterprise Ethereum Alliance (EEA). Sandra holds an M. She advises and supports several women, children, education, animal conservation-focused non-profits in the USA, UK, and sub-Saharan Africa. Prior to Roku, Scott was CEO of Umami, a VC-backed companion TV startup. Before that, he held senior product and engineering roles at Rovi, BlackArrow, ReplayTV and Intel.
Scott has spoken at many industry conferences including VideoNuze, IAB Leadership Forum and other key events. Jo Ann Ross is a broadcast network sales veteran who has served in various sales roles at CBS over the last 25 years. Ross, the first woman to serve as sales chief of a broadcast network and the longest running sales head in broadcast television,was promoted to President and Chief Advertising Revenue Officer, CBS Corporation in August 2017. Ross previously served as President, Network Sales, CBS Television Network since October 2002, responsible for overseeing all sales for CBS Entertainment, Sports, Daytime, News and Late Night.
In addition to her new responsibilities, Ross will continue to run the Network sales team in its many operations.
She reports to Leslie Moonves, Chairman and CEO, CBS Corporation. She also played a major role in creating new ways to sell Network programming, including reality, scripted, news and late-night genres. She joined CBS in 1992 as Vice President of Olympic Sales, in charge of Network sales for the Lillehammer Winter Olympics in 1994 and the Nagano Winter Olympics in 1998. Prior to joining CBS, Ross was with the ABC Network for three years, where she sold daytime before being named a Prime Account Executive.
Before that, Ross worked at the media buying firm Bozell, starting as an assistant buyer and working her way up to Senior Vice President, where she ran the network buying department. She also serves on the Board of Directors of the John A. Reisenbach Foundation and The Ad Council.
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He is a graduate of the City College of New York. Randall Rothenberg is the president and CEO of the Interactive Advertising Bureau, the trade association for interactive marketing in the United States. The IAB represents over 600 leading media, marketing and technology companies.
Its members include Google, Yahoo, Microsoft, AOL, The New York Times, Walt Disney Co. The IAB has 45 affiliate associations around the world. Rothenberg led the IAB from 2007 through 2010, and rejoined the association in March 2011, after a stint as Executive Vice President and Chief Digital Officer of Time Inc.
Prior to his IAB role, Mr.A Winter Storm Warning is in effect for the city and surrounding areas from 4 a. Saturday to 7 a. Still looks like 3-6" of snow for much of the region tomorrow. A lone woman makes her way in Boston's South End neighborhood during a storm last March.
Check out the conversations on Boston. Sign up for Boston. Connect with Facebook - or - Thanks for signing up. Finds Extreme Poverty and Human Rights In an Unexpected Place by Robby Berman Playing Super Mario 64 Increases Brain Health in Adults by Stephen Johnson Scientists Link 2 Genes to Homosexuality in Men by Robby Berman Loading.
That's great, but many of those predictions will be hopelessly wrong by the end of March. That's why it's so fascinating that Ray Kurzweil, one of the leading thinkers when it comes to the future of technology, has had such a strong track record in making predictions about technology for nearly two decades. So how does he do it. The fact is, Ray has a system and this system is called the Law of Accelerating Returns.
In his new book How to Create a Mind: The Secret of Human Thought Revealed, Kurzweil points out that "every fundamental measure of information technology follows predictable and exponential trajectories. Thanks to paradigms such as Moore's Law, which reduces computing power to a problem of how many transistors you can cram on a chip, anyone can intuitively understand why computers are getting exponentially faster and cheaper over time. The other famous exponential growth curve in our lifetime is the sheer amount of digital information available on the Internet.
Kurzweil typically graphs this as "bits per second transmitted on the Internet. That's why "Big Data" is such a buzzword these days - there's a growing recognition that we're losing track of all the information we're putting up on the Internet, from Facebook status updates, to YouTube videos, to funny meme posts on Tumblr.
In just a decade, we will have created more content than existed for thousands of years in humanity's prior experience. And it's not just computing power or the growth of the Internet. Chapter ten in Kurzweil's latest book, How to Create a Mind, includes 15 other charts that show these exponential growth curves at work.
Once any technology becomes an information technology, it becomes subject to the Law of Accelerating Returns. Consider biomedicine, for example. Now that the human genome is being translated into a digital life code of 1's and 0's that can be processed by computers, it's also an information technology, and that means it's also subject to the Law of Accelerating Returns.
When you look at the cost of sequencing a human-sized genome, the cost started dropping exponentially around 2001 and fell off the genomic cliff in about 2007 -- about the same time that Craig Venter's genome project took off. As Ray points out in How to Create a Mind, the reason why typical pundits and prognosticators typically get it wrong year after year is that the human mind has evolved to think linearly, not exponentially.
We conceive of 40 steps as a linear progression: one step after another, from 1 to 40. Ray thinks exponentially, though.
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