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Try our fixed-odds football betting service Superscore. RACE WINNER PLACE WIN 1 2 3 4 5 6 7 8 Win Bet on the horse that you think will take the first place in the race. For example, a bet would read as follows: Race 1, Win, No. Read more Place Bet on the horse which will be one of the first three.
For example, a bet would read as follows: Race 2, Place, No. Read more Exacta Choose two horses finishing 1st and 2nd in the correct order in a race. Read more Swinger Choose two horses among the first three in a same race without having to give any indication of their order at finish.
Read more Trifecta Choose three horses finishing 1st, 2nd and 3rd in the correct order in a race. Read more Quartet Four horses finishing 1st, 2nd, 3rd and 4th in the correct order in a race.
Read more Double The purpose of the Double Totalisator is to select the horses finishing first in each of the two legs making up the Double. Read more Pick 4 Select a horse to finish first in each of 4 legs of the Pick 4 pool. Read more Pick 6 Select a horse to finish first in each of 6 legs of the Pick 6 pool. Read more Pick 8 Select a horse to finish first in each of 8 legs of the Pick 8 pool.
Read more Place Accumulator Select a placed horse in each of the seven Place Accumulator races. Snowy Bruce is a front runner, a horse racing trend-setter at the top of his game.
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This guide goes over the three best ways to approach Reading passages and offers tips on how to answer the questions that accompany them. How to Answer ACT Reading Questions: 5-Step GuideNeed help getting questions right on ACT Reading. In this resource, we explain how to break down Reading questions and eliminate incorrect answer choices.
We also go over the common ways ACT Reading likes to trick test takers. How to Stop Running Out of Time on ACT ReadingTime management can be difficult on ACT Reading, which only gives you about 53 seconds per question. Our guide explains how to monitor your time and practice effectively so that you're getting more questions right before time runs out.
We summarize what it takes to get a 26 on Reading, explain how ACT Reading likes to trick test takers, and give you eight strategies to help you get a higher score. How to Get 36 on ACT Reading: 11 Strategies From a Perfect ScorerAiming for perfection.
Then check out our guide to getting a 36 on ACT Reading, written by an actual full scorer. With this resource, we teach you how to identify and target your weaknesses as well as how to find the best passage-reading strategy for you.Sfide hector bellerin fifa 21
The truth is that, despite its name, ACT Science is way more about reading than it is about actual science. The Ultimate Study Guide for ACT Science: Tips, Practice, and StrategiesFor a robust resource of all of our best ACT Science articles, check out our ultimate ACT Science study guide. We give you links to a variety of articles and guides focusing on the format of ACT Science, what it tests, and how you can prepare effectively for it.
This guide summarizes the five possible strategies you can use and shows you how to find the perfect fit for you. So what science do you actually need to know for it. We teach you what order to read the passages in, the maximum amount of time you should spend on a question, and how to keep your energy level up on test day. Don't worry because there are plenty of ways to raise it.
How to Get 36 on ACT Science: 13 Strategies From a Perfect ScorerAiming higher than a 26. Then check out our guide to getting a perfect ACT Science score, written entirely by an ACT expert and full scorer. In this guide, we provide you with 13 key strategies for reaching perfection in addition to examples of schools that expect perfect Science scores. Should You Take the ACT With or Without Writing.
But what should you do. Read this guide to get advice on whether you should take the ACT essay.
We go over key factors to consider, such as the price of the ACT with Writing and whether a high Writing score could potentially boost your application. This step-by-step guide takes you through the entire process of composing a strong ACT essay, from planning and brainstorming to organizing paragraphs and revising your work. The Ultimate ACT Writing Study Guide: Tips, Strategies, and PracticeIf you prefer having all of your ACT Writing resources in one place, this is the guide for you.
We explain what the ACT wants you to write about in your essays and go over the different ways essay prompts are often worded. ACT Writing Tips: 15 Strategies to Raise Your Essay ScoreGetting a high score on ACT Writing can be challenging, but as long as you know what graders are looking for, you should have no problem raising your score.
Read this guide to learn all of our best tips and strategies for getting a solid essay score. How to Get a Perfect 12 on the ACT Writing EssayCraving a perfect essay score. This comprehensive guide explains what a 12 on the ACT essay means, elaborates on how a 12 differs from a 10 (even though both are high scores. How many times should you take it.
How many weeks or months should you spend studying. Our resources below will help you plan your ACT prep. In addition, we give you links to high-quality study materials you can incorporate into your prep plan. When Should You Take the ACT.I think he bounces back and makes some throws that we haven't seen in a month.
Cowboys, 24-20Brandon GeorgeSure, the Giants will get a little surge from the return of Eli Manning as the starting quarterback and the coaching change. Cowboys, 27- 20Kate HairopoulosThe playoff scenarios already look dismal, but the recent win over Washington offered a peek of hope with a month of games to play. Cowboys, 24- 17 Barry HornThe Cowboys would appear to be slam-dunk winners against the hapless Giants.
Cowboys, 27-26Jon MachotaI expect Eli Manning's return to the starting lineup to bring an emotional lift to the Giants, allowing them to build an early lead. Cowboys, 21-17David MooreThe Giants have a new head coach. Cowboys, 23-16Kevin SherringtonEli Manning makes his not-so-triumphant return just as the Cowboys figure out who they are.
Cowboys, 21-17Bob SturmThe Giants could absolutely show up in any condition on Sunday and it should not surprise us with the chaos surrounding their franchise. With 10 minutes left before the end of the game and with City leading 1-0, Tinkler introduced Thabo Mnyamane for Reneilwe Letsholonyane and within seconds on the park, Mnyamane grabbed the equaliser for SuperSport.
Matsatsantsa then went on to win 4-2 on penalties. SuperSport have only suffered one loss in all competitions at home this season, with that loss coming from neighbours Sundowns in August last year. Matsatsantsa have since went on an eight-match unbeaten run in all competitions at home after that loss. City got off to a bright start to life under McCarthy, reaching the MTN8 final but they have won only one of their last six league games, with their dramatic second half collapse at home to Maritzburg United in midweek particularly concerning.
City will need to bring far more defensive resolve to Tshwane on Saturday, as they seek redemption for the MTN8 final defeat to SuperSport on October 14. City have only won two away games in the league this season, at Wits in the first round of fixtures, and at Ajax, though that was effectively on their home ground at Cape Town Stadium.
But I just fancy them to add a third today. Matsatsantsa are on a good run under Eric Tinkler, unbeaten in seven league games, but I think that will come to an end, with City winning 2-1. Follow the game live HERE. Would you like to place a soccer bet.
Click here to get started in 3 easy steps. Why SuperSport will win (Khaya Ndubane, Online Content Manager) Coach Eric Tinkler has already tasted victory against his former team City albeit through a penalty shootout in the final of the MTN8 in October. Prediction: SuperSport United 1 Cape Town City 2 Follow the game live HERE. Man UnitedMan CityDraw googletag. Holding the sign is five-year-old Melville Grosvenor, future editor of National Geographic.
Thomas Selfridge, became the first person to die in a plane crash when he tested the 1908 Wright Military Flyer the following year. He usually escaped to his estate in Nova Scotia, but one year obligations forced him to stay in the humid capital. Nearby, President Woodrow Wilson had installed an ice plant that lowered the temperature in the White House to 80 degrees. By the time Bell read about it in the newspaper, he had already outdone the president.
He then went on to predict the commercial airplane, solar panels, and the need for renewable resources. But the past century had birthed extraordinary discoveries, from the telegraph to the photograph.15-99-00 sort code address
Bell made daily additions to the legendary notebooks: sketches of inventions, musings, press clippings. Others were huge undertakings: Bell spent decades trying to breed sheep with more than two nipples.Container port throughput in 2016: In 2016, Asian economies continue dominating the global port traffic. Four out of the five economies with the most port traffic were from Asia. The global leader, China, recorded a total of 200 million TEU - four times the port traffic of the United States (48 million TEU).
Total merchandise trade in 2016: New data show a decrease of total merchandise trade by 3. Especially the drop of LDCs' and LLDCs' exports turns out less severe.
The new figures rely on a broader basis of input data than the spring release. World Toilet Day is about inspiring action to tackle the global sanitation crisis. More Statistics and SDGs The UNCTAD Development and Globalization: Facts and Figures 2016 is dedicated to the Sustainable Development Goals that were adopted by the United Nations in September 2015 (2030 Agenda Declaration).
Statistics capacity building UNCTAD assists national statistical agencies in developing and transition countries in their data collection and dissemination efforts. UEMOA - International Trade in Services Statistics E-learning Tool on international trade in services statistics More. NewsTrade and Development in Numbers: Handbook offers crucial economic data for global decision-makersUNCTAD has launched its annual Handbook of Statistics in a new, user-friendly format to celebrate its fiftieth year of publication.
The 2017 edition has been overhauled entirely to make it shorter and easier to read, and features a wealth of maps, charts, and infographics. More Statistics capacity building UNCTAD assists national statistical agencies in developing and transition countries in their data collection and dissemination efforts.
Latest key facts Container port throughput in 2016: In 2016, Asian economies continue dominating the global port traffic.
News Trade and Development in Numbers: Handbook offers crucial economic data for global decision-makersUNCTAD has launched its annual Handbook of Statistics in a new, user-friendly format to celebrate its fiftieth year of publication. As one of the major statistics departments in New England, it provides outstanding preparation for careers in academia, industry, or government.
With a core faculty of 20 professors whose teaching and research interests span virtually all major statistical specializations, our department has received national and international recognition in graduate education and research. Graduates from UConn have found excellent positions in academics, government, and industry. We maintain close relationships with area companies and institutions, and our department is an active member of the American Statistical Association Connecticut chapter.
Paper of the MonthWe are excited to announce the Paper of the Month:Once a month during the academic year our faculty will select a paper which we encourage our students to read and discuss. Papers featured in this section should be generally understood by graduate students, and will be selected either because of their impact, or historical value, or because they contain a useful (perhaps overlooked) techniques or results.
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Austin Building University of Connecticut 215 Glenbrook Rd. The Department of Statistics at the University of Connecticut was founded in 1962. We are excited to announce the Paper of the Month: Once a month during the academic year our faculty will select a paper which we encourage our students to read and discuss.
Will resume in the Spring 2018 semester. Colloquia are held at 4pm in AUST 105. Coffee will be served at 3:30pm in AUST 326. Storrs, Mansfield, Connecticut Mailing Address (Mail Stop): Room 323, Philip E.When a predicate for a numeric field is given, the field will be discretized along bin edges specified by the predicate.
With the above example, the field petal width will be discretized into three bins, corresponding to the values 2. If a predicate is given without an operator or value, then any item pertaining to this field is accepted into the RHS.
Discretization is used to transform numeric input fields to categoricals before further processing. You can also use curl to customize a new association. Once an association has been successfully created it will have the following properties. Creating an association is a process that can take just a few seconds or a few days depending on the size of the dataset used as input and on the workload of BigML's systems.
The association goes through a number of states until its fully completed. Through the status field in the association you can determine when the association has been fully processed and ready to be used to create predictions.
Thus when retrieving an association, it's possible to specify that only a subset of fields be retrieved, by using any combination of the following parameters in the query string (unrecognized parameters are ignored): Fields Filter Parameters Parameter TypeDescription fields optional Comma-separated list A comma-separated list of field IDs to retrieve.
To update an association, you need to PUT an object containing the fields that you want to update to the association' s base URL. Once you delete an association, it is permanently deleted. If you try to delete an association a second time, or an association that does not exist, you will receive a "404 not found" response.
However, if you try to delete an association that is being used at the moment, then BigML. To list all the associations, you can use the association base URL.Filmmaking course in india
By default, only the 20 most recent associations will be returned. You can get your list of associations directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your associations. Topic Models Last Updated: Monday, 2017-10-30 10:31 A topic model is an unsupervised machine learning method for unveiling all the different topics underlying a collection of documents. BigML uses Latent Dirichlet allocation (LDA), one of the most popular probabilistic methods for topic modeling.
In BigML, each instance (i. If multiple text fields are given as inputs, they will be automatically concatenated, so the content for each document can be considered as a bag of words. Topic model is an unsupervised method so your data doesn't need to be labeled.
Topic model is based on the assumption that any document exhibits a mixture of topics. Each topic is composed of a set of words which are thematically related.
The words from a given topic have different probabilities for that topic. At the same time, each word can be attributable to one or several topics. So for example the word "sea" may be found in a topic related with sea transport but also in a topic related to holidays.
Topic model automatically discards stopwords and high frequency words that occur in almost all of the documents as they don't help to determine the boundaries between topics. Topic model's main applications include browsing, organizing and understanding large archives of documents. It can been applied for information retrieval, collaborative filtering, assessing document similarity among others. The topics found in the dataset can also be very useful new features before applying other models like classification, clustering, or anomaly detection.
Topic model returns a list of top terms for each topic found in the data. Note that topics are not labeled, so you have to infer their meaning according to the words they are composed of. By looking at each group of terms below we can interpret the first topic as regulatory related, the second as healthcare related and so on. You can obtain up to 128 different topics. Once you build the topic model you can calculate each topic probability for a given document by using Topic Distribution.
This information can be useful to find documents similarities based on their thematic.You can use BigML. That is to say, using BigML. Fully white-box access to your datasets, models, clusters and anomaly detectors. Asynchronous creation of resources. The four original BigML resources are: source, dataset, model, and prediction. As shown in the picture below, the most basic flow consists of using some local (or remote) training data to create a source, then using the source to create a dataset, later using the dataset to create a model, and, finally, using the model and new input data to create a prediction.
The training data is usually in tabular format. Each row in the data represents an instance (or example) and each column a field (or attribute). These fields are also known as predictors or covariates. When the machine learning task to learn from training data is supervised one of the columns (usually the last column) represents a special attribute known as objective field (or target) that assigns a label (or class) to each instance. The training data in this format is named labeled and the machine learning task to learn from is named supervised learning.
Once a source is created, it can be used to create multiple datasets. Likewise, a dataset can be used to create multiple models and a model can be used to create multiple predictions. A model can be either a classification or a regression model depending on whether the objective field is respectively categorical or numeric. Often an ensemble (or collection of models) can perform better than just a single model.
Thus, a dataset can also be used to create an ensemble instead of a single model. A dataset can also be used to create a cluster or an anomaly detector. Clusters and Anomaly Detectors are both built using unsupervised learning and therefore an objective field is not needed.
In these cases, the training data is named unlabeled. A centroid is to a cluster what a prediction is to a model. Likewise, an anomaly score is to an anomaly detector what a prediction is to a model. There are scenarios where generating predictions for a relative big collection of input data is very convenient. For these scenarios, BigML. These resources take a dataset and respectively a model (or ensemble), a cluster, or an anomaly detector to create a new dataset that contains a new column with the corresponding prediction, centroid or anomaly score computed for each instance in the dataset.
Note: In the snippets below you should substitute Alfred's username and API key for your own username and API Key. You can create, read, update, and delete resources using the respective standard HTTP methods: POST, GET, PUT and DELETE. All communication with BigML. All access to BigML. In this way communication between your application and BigML.
In development mode, you do not consume any credits and you cannot create datasets that are bigger than 16 MB each even if you can create sources of any size. If you omit the version name in your API requests, you will always get access to the latest API version. While we will do our best to make future API versions backward compatible it is possible that a future API release could cause your application to fail.
Operation HTTP method Semantics CREATE POST Creates a new resource. Only certain fields are "postable". This method is not idempotent. Each valid POST request results in a new directly accessible resource. RETRIEVE GET Retrieves either a specific resource or a list of resources.
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