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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.

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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 also list all of your topic models. Specifies a list of terms to ignore when performing term analysis. This can be used to change the names of the fields in the topic model with respect to the original names in the dataset or to tell BigML that certain fields should be preferred.

All text fields in the dataset Specifies the fields to be considered to create the topic model. If multiple fields are given, the text field values for each row will be concatenated so that each row is still considered to be one document.

If it is unset, it will be chosen automatically based on the number documents (i. The minimum value is 2 and maximum value is 64. Example: "MySample" tags optional Array of Strings A list of strings that help classify and index your topic model. Computation is linear with respect to this parameter. The minimum value is 128 and maximum value is 16384. The minimum value is 1 and maximum value is 128.

Example: true You can also use curl to customize a new topic model. Once a topic model has been successfully created it will have the following properties. Topic Model Status Creating a topic model 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 topic model goes through a number of states until its fully completed. Through the status field in the topic model you can determine when the topic model has been fully processed and ready to be used to create predictions. Thus when retrieving a topicmodel, 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 a topic model, you need to PUT an object containing the fields that you want to update to the topic model' s base URL.This is the date and time in which the dataset was updated with microsecond precision. Dataset Fields The property fields is a dictionary keyed by each field's id in the source. Each field's id has as a value an object with the following properties: Numeric summaries come with all the fields described below.

If the number of unique values in the data is greater than 32, then 'bins' will be used for the summary. If not, 'counts' will be available. Categorical summaries give you a count per each category and missing count in case any of the instances contain missing values. Text summaries give statistics about the vocabulary of a text field, and the number of instances containing missing values.

Before a dataset is successfully created, BigML. The dataset goes through a number of states until all these analyses are completed. Through the status field in the dataset you can determine when the dataset has been fully processed and ready to be used to create a model. It includes the total row-format errors and a sampling of the ill-formatted rows. Thus when retrieving a dataset, 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.

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To update a dataset, you need to PUT an object containing the fields that you want to update to the dataset' s base URL. Once you delete a dataset, it is permanently deleted. If you try to delete a dataset a second time, or a dataset that does not exist, you will receive a "404 not found" response. However, if you try to delete a dataset that is being used at the moment, then BigML. To list all the datasets, you can use the dataset base URL.

By default, only the 20 most recent datasets will be returned. You can get your list of datasets directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your datasets. Imagine, for example, that you collect data in a hourly basis and want to create a dataset aggregrating data collected over the whole day. So you only need to send the new generated data each hour to BigML, create a source and a dataset for each one and then merge all the individual datasets into one at the end of the day.

We usually call datasets created in this way multi-datasets.Long may it continue. I don't think so. The best tipster out there at the moment, keep up the good work Craig.

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Opt out Complete This campaign ended on July 13, 2014 at 12PM Support bettingexpert in sharing this message. Support with FACEBOOK Support with TWITTER Support with TUMBLR We will post this one-time message to your account onJuly 13 at 12:00PM CEST. In the latest exchange between the two celebrities-turned-politicians, Arnold Schwarzenegger mocks Donald Trump over his slumping approval rating.

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Datasets Last Updated: Monday, 2017-10-30 10:31 A dataset is a structured version of a source where each field has been processed and serialized according to its type. The possible field types are numeric, categorical, text, date-time, or items. For each field, you can also get the number of errors that were encountered processing it.

Errors are mostly missing values or values that do not match with the type assigned to the column. When you create a new dataset, histograms of the field values are created for the categorical and numeric fields. In addition, for the numeric fields, a collection of statistics about the field distribution such as minimum, maximum, sum, and sum of squares are also computed.

For date-time fields, BigML attempts to parse the format and automatically generate the related subfields (year, month, day, and so on) present in the format. For items fields which have many different categorical values per instance separated by non-alphanumeric characters, BigML tries to automatically detect which is the best separator for your items. We are then left with somewhere between a few dozen and a few hundred interesting words per text field, the occurrences of which can be features in a model.

You can also list all of your datasets. The former specifies the list of fields to be included in the dataset, and defaults to all fields in the source when empty. That is, no names, labels or descriptions are changed. Updates the names, labels, and descriptions of the fields in the dataset with respect to the original names in the source. An entry keyed with the field id generated in the source for each field that you want the name updated.

All the fields in the source. Specifies the fields to be included in the dataset. The first element is an operator and the rest of the elements its arguments. See the section below for more details. Specifies the default objective field. Example: true size optional Integer,default is the source's size The number of bytes from the source that you want to use.

Example: 500 You can also use curl to customize a new dataset with a name, and different size, and only a few fields from the original source. If you do not specify a size, BigML. If you do not specify any fields BigML. This predicate is specified as a (possibly nested) JSON list whose first element is an operator and the rest of the elements its arguments. Here's an example of a filter specification to choose only those rows whose field "000002" is less than 3.

Note how you're not limited to two arguments. It's also worth noting that for a filter like that one to be accepted, all three fields must have the same optype (e. The field operator also accepts as arguments the field's name (as a string) or the row column (as an integer).

If you have duplicated field names, the best thing to do is to use either column numbers or field identifiers in your filters, to avoid ambiguities. Besides a field's value, one can also ask whether it's missing or not. These are all the accepted operators: To be accepted by the API, the filter must evaluate to a boolean value and contain at least one operator. So, for instance, a constant or an expression evaluating to a number will be rejected.

Once a dataset has been successfully created it will have the following properties. This is the date and time in which the dataset was created with microsecond precision. It can contain restricted markdown to decorate the text.

It has an entry per each field type (categorical, datetime, numeric, and text), an entry for preferred fields and an entry for the total number of fields. That is the total number of fields including those created under the hood to support text fields.Whenever you are having a hard time staying in the now, take deep breaths, and focus on your breathing. You could even count your breaths.

Something I like to do is count to four on the inhale and four on the exhale. It focuses the logical part of my brain on counting and allows me to focus on my breath. After a while I can release the crutch of counting and just be. In fact, they are rarely right, especially if they make you feel bad. Question your thoughts constantly. When you start to feel negative emotions, use it as a reminder to examine what thoughts are causing the commotion. Most people walk around all day letting negative thoughts cause negative feelings.

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You will face challenges, obstacles, and problems. I have noticed that the more I follow my passion and my hearts deepest desire, the more I am able to stay in the now. My heart buzzes with joy and I feel amazing. Writing is one of my passions. I love helping people improve their life. It makes me come alive, and it keeps me in the present moment.

Find your passion and go after it. Becoming more mindful is done step-by-step. You do not have to go all-in. You only have to increase the amount of time you spend in the present moment each and every day. Henri writes at Wake Up Cloud, where you can get his free course: 7 Steps to Building a Lifestyle Business Around Your Passion. He's also the author of Find Your Passion: 25 Questions You Must Ask Yourself and Follow Your Heart: 21 Days to a Happier, More Fulfilling Life. I love reading your things every day.

They are helping me grow.Press Release18 Sep 17 22nd UNWTO General Assembly in China: a week of important achievementsAn intense week of meetings, decisions and agreements marked the 22nd session of the UNWTO General Assembly in Chengdu, China on 13-16 September. The biennial event convened more than 1300. World Committee on Tourism EthicsWorld Tourism BarometerWorld Tourism DayRegional ProgrammesAfricaAmericasAsia and the PacificEuropeMiddle East. If the address matches an existing account you will receive an email with instructions to reset your passwordIf the address matches an existing account you will receive an email with instructions to retrieve your username To submit proposals to either launch new journals or bring an existing journal to MIT Press, please contact Director for Journals and Open Access, Nick Lindsay.

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Careful ongoing analysis of our admissions statistics shows that, for equally well-qualified applicants, making an open application or applying directly to a College does not affect your chance of being made an offer of a place. This is because we have rigorous procedures in place to compare all applicants for each subject before selection decisions are finalised.

Colleges would rather admit a strong applicant from the pool than a weaker applicant who applied directly to them. See further information regarding how to choose a College.

This is one of the most frequently asked questions about applying to Cambridge. On average across all subjects, we typically receive five applications per place, but naturally there is some variation between courses. The University and Colleges are committed to offering admission to students of the highest academic ability and potential. Despite application numbers varying considerably each year, our system means that success rates are very similar from College to College.

This is because the pool results in many students (938 in the case of the 2017 cycle, about 21 per cent of all offers made) receiving an offer from a College other than the one they applied to, or were allocated to through the open application system.

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Please note the source data for these graphs was last refreshed on 22 November 2017. Use our interactive graph generator below to view basic undergraduate application and admissions statistics using criteria that you define.

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