Mpl indonesia champion 2020

Coming with billions of rupiah prize, MPL Indonesia has a commitment to always give the best every season. These 8 teams will compete for the total prize over IDR 4 billion.

As part of MPL ongoing commitment to provide a higher access to the league matches, Season 5 will be broadcasted on multiple live streaming platforms such as Facebook, Nimo TV, and YouTube. We are also proud to announce that MPL ID and our franchised teams has managed to bag a series of awards in This can be achieved thanks to the love and great ambition of MPL teams whom continued to improve themselves to be the best and to join the most prestigious competitive stage in Indonesia, MPL ID and the support of our fans.

Dynamic competition and a stable esports ecosystem are two things that distinguish MPL ID from other esports events in Indonesia. One of the most most important ingredient to the league success is listening to our fans. The creation of MDL is a direct result of listening to our fans and in this Season 5, we will focus on maximizing the user experience, for both the audience and the teams.

This is considered successful because all parties involved in it have become increasingly professional in carrying out their respective duties. Within a year, realme has launched trendy smartphone series with best-in-class performance to provide immersive gaming experience in various price segments. This partnership marks our huge step in esports, especially to encourage the mobile gaming industry and also young and potential gamers to enjoy greater gaming experience through our products.

Last but not least, best of luck for all 8 professional teams of MPL Season 5. Keep fighting for victory, see you all at the Playoffs! Collaboration carried out by realme and MPL is based on the same spirit to advance the technology and gaming industry in Indonesia. Both realme and MPL are young, trendy, and aggressive brands in terms of pursuing their respective goals. These values are really reflected in their products, without a doubt they are the best for high-skilled MPL fans that aims to become professional gamers.

MDL is one of Moonton's efforts to form a regeneration space for new esports talents on all fronts - both in terms of players and management. Considering that regeneration is not an easy task, Moonton also added a new competitive event in aimed at students.

Driven by the determination to elevate the esports ecosystem, in this fourth season, MPL is making the leap by constructing the first franchised-model esports league in Southeast Asia.

Ensuring the sustainability of the overall community, this franchise model will implement the revenue sharing, salary cap, and other special benefits to the participating teams. About realme realme is a technology brand that focuses on providing high-quality smartphones with a Dare-to-Leap experience. The brand was officially founded on May 4,founder of Sky Li, along with a group of young people who have rich experience in the smartphone industry.

And has gained 10 million users in just one year, making realme the fastest growing smartphone brand. For more information, visit www. About Mineski Indonesia Mineski Global, the largest esports organization in Southeast Asia dan its affiliate Mineski Indonesia, recently unveiled their new corporate logo in support of their ambitious plans riding on the phenomenal growth of esports.

About Secretlab Secretlab was established in to create the pinnacle of gaming seats—each extensively designed and engineered with only materials of the highest grade to ensure absolute comfort and unparalleled support. To learn more about us, visit www. Back to News.For example, to create a new batch prediction named "my batch prediction", that will not include a header, and will only output the field "000001" together with the confidence for each prediction.

Once a batch prediction has been successfully created it will have the following properties. Creating a batch prediction is a process that can take just a few seconds or a few hours depending on the size of the dataset used as input and on the workload of BigML's systems.

The batch prediction goes through a number of states until its finished. Through the status field in the batch prediction you can determine when it has been fully processed.

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Once you delete a batch prediction, it is permanently deleted. If you try to delete a batch prediction a second time, or a batch prediction that does not exist, you will receive a "404 not found" response. However, if you try to delete a batch prediction that is being used at the moment, then BigML. To list all the batch predictions, you can use the batchprediction base URL.

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By default, only the 20 most recent batch predictions will be returned. You can get your list of batch predictions directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your batch predictions. Batch Centroids Last Updated: Monday, 2017-10-30 10:31 A batch centroid provides an easy way to compute a centroid for each instance in a dataset in only one request.

Batch centroids are created asynchronously.

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You can also list all of your batch centroids. You can easily create a new batch centroid using curl as follows. All the fields in the dataset Specifies the fields in the dataset to be considered to create the batch centroid. Example: "my new batch centroid" newline optional String,default is "LF" The new line character that you want to get as line break in the generated csv file: "LF", "CRLF".

For example, to create a new batch centroid named "my batch centroid", that will not include a header, and will only ouput the field "000001" together with the distance for each centroid. Once a batch centroid has been successfully created it will have the following properties.

Creating a batch centroid is a process that can take just a few seconds or a few hours depending on the size of the dataset used as input and on the workload of BigML's systems.

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The batch centroid goes through a number of states until its finished.Once you delete a centroid, it is permanently deleted. If you try to delete a centroid a second time, or a centroid that does not exist, you will receive a "404 not found" response.

However, if you try to delete a centroid that is being used at the moment, then BigML. To list all the centroids, you can use the centroid base URL. By default, only the 20 most recent centroids will be returned.

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You can get your list of centroids directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your centroids.

When you create a new anomaly score, BigML. The closer the score is to 1, the more anomalous the instance being scored is. That is, how much each value in the input data contributed to the score. You can also list all of your anomaly scores.

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You can use curl to customize new anomaly scores. Once an anomaly score has been successfully created it will have the following properties. Creating an anomaly score is a near real-time process that take just a few seconds depending on whether the corresponding anomaly has been used recently and the workload of BigML's systems. The anomaly score goes through a number of states until its fully completed. Through the status field in the anomaly score you can determine when the anomaly score has been fully processed and ready to be used.

Most of the times anomaly scores are fully processed and the output returned in the first call. These are the properties that an anomaly score's status has:To update an anomaly score, you need to PUT an object containing the fields that you want to update to the anomaly score' s base URL. Once you delete an anomaly score, it is permanently deleted. If you try to delete an anomaly score a second time, or an anomaly score that does not exist, you will receive a "404 not found" response.

However, if you try to delete an anomaly score that is being used at the moment, then BigML. To list all the anomaly scores, you can use the anomalyscore base URL. By default, only the 20 most recent anomaly scores will be returned. You can get your list of anomaly scores directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your anomaly scores. Association Sets are useful to know which items have stronger associations with a given set of values for your fields.

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The similarity score then is multiplied by the selected association measure (confidence, leverage, support, lift, or coverage) to create a similarity-weighted score and finally return a ranking of the predicted items. You can also list all of your association sets. You can use curl to customize new association sets. Once an association set has been successfully created it will have the following properties.

Creating an association set is a near real-time process that take just a few seconds depending on whether the corresponding association has been used recently and the workload of BigML's systems. The association set goes through a number of states until its fully completed. Through the status field in the association set you can determine when the association set has been fully processed and ready to be used.

Most of the times association sets are fully processed and the output returned in the first call.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. It includes a very intuitive description of the tree-like structure that makes the model up and the field's dictionary describing the fields and their summaries.

In a future version, you will be able to share models with other co-workers or, if desired, make them publicly available. This is the date and time in which the model was updated with microsecond precision.

A Model Object has the following properties: Creating a 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 model goes through a number of states until its fully completed. Through the status field in the model you can determine when the model has been fully processed and ready to be used to create predictions.

Support is a number from 0 to 1 that specifies the minimum fraction of the total number of instances that a given branch must cover to be retained in the resulting tree.

If you repeat the support parameter in the query string, the last one is used. Non-parseable support values are ignored. Value is a concrete value or interval of values (for regression trees) that a leaf must predict to be kept in the returning tree.

Intervals can be closed or open in either end. Confidence is a concrete value or interval of values that a leaf must have to be kept in the returning tree.

The specification of intervals follows the same conventions as those of value. Since confidences are a continuous value, the most common case will be asking for a range, but the service will accept also individual values. It's also possible to specify both a value and a confidence.

Finally, note that it is also possible to specify support, value, and confidence parameters in the same query. Filtering and Paginating Fields from a Model A model might be composed of hundreds or even thousands of fields. Thus when retrieving a model, 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 model, you need to PUT an object containing the fields that you want to update to the model' s base URL. Once you delete a model, it is permanently deleted. If you try to delete a model a second time, or a model that does not exist, you will receive a "404 not found" response.

However, if you try to delete a model that is being used at the moment, then BigML. To list all the models, you can use the model base URL. By default, only the 20 most recent models will be returned. You can get your list of models directly in your browser using your own username and API key with the following links.

You can also paginate, filter, and order your models. This is valid for both regression and classification models.AfterShip is excellent, can't recommend it highly enough. Worth the money ten times over. It blows the actual shipping companies tracking and other information out of the water.

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I admit, in the 2 weeks I was a bit slack with regular exercise as I had other things going on (no excuse, I know) but I have been eating healthier with smaller portions. There's not much of a difference as I was slim to start with, but this gave me the jump start to get the body I want, couldn't recommend it more. Definitely going to go for the 28 day next time. Also the tea tasted lovely :) quite upset that I've now finished it. I haven't finished my health journey yet, it's just getting started.

Alana BeatonTo be honest I was freaking out a bit because I was like "crap I promised the instagram world before and after shots and I'm gonna look exactly the same.

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