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Eshghe Atashi Mansour. Composer : Siavash Ghomeishi Poet : Paksima.Leila Forouhar - Chi Seda Konam - لیلا فروهر - چی صدا کنم تو رو
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Explain to us exactly what part or parts of that material infringe your copyright. Sign the notice. Send the notice to our email address which can be found in the contact page.Once that required computing power was possible (thanks to Moore's Law), it was time to move on to the next challenge -- becoming a Jeopardy.Tisas zigana fc
Now, with the victory of Watson, it's time to move on to the next challenge - becoming the world's best doctor. The really exciting feature of the Law of Accelerating Returns is that it implicity assumes that one exponential technology builds on top of the next exponential technology.
Something like 3D printing is an example of one exponential technology building on top of another exponential technology. In fact, 3D printing may turn out to be the ultimate exponential technology for the coming 12 months, so much so that WIRED's Chris Anderson is betting his reputation on it.
So what other areas could be ripe for surprise breakthroughs next year, due to exponential leaps in computing power.Sinclair broadcast group stations
Untitled Created with Sketch. Big Think Edge helps organizations by catalyzing conversation around the topics most critical to 21st century business success. This Calculator Can Tell You Ketogenic Diets Have "Profound Effect on Brain Function," Studies Find Dare to Think Bigger Sign up to get more stories like this in your inbox RELATED by Frank Jacobs by Teodora Zareva by Frank Jacobs by Frank Jacobs by Frank Jacobs by Big Think Editors by Frank Jacobs by Frank Jacobs Untitled Created with Sketch.
Democrats are wondering where to go after Trump on: appropriateness, competence, corruption, and work ethic. The answer is that people are evenly divided on these topics. Republicans support him and Democrats oppose him. Yet, there are some key points.
Poll was run by PredictWise with Pollfish on May 22, 2017. Yet, even a few days into the scandal, his probability of reaching 2020 as president is still 50 percent. My update from today. Clinton is far enough up, and there are few enough persuadable voters, that she will need to lose support for Trump to win (not just Trump gain support). Almost no one will switch between Clinton and Trump, but some people could switch to non-voters or third party candidates.
As I note above, this is unlikely Special Facebook Live. Markets still have some uncertainty as they worry that Comey is a rational person who would have no sent world-wide markets plunging for no good reason. Hope is fading fast on that. Will post evening wrap in a 1-2 hours.
Polls are stable, with tight NC.
One exception is Clinton pulling away in NV. Latest Facebook Live from October 20. Polls are holding steady, as Trump hits ceiling on GOP voters, but does not really go down much.Anomaly Detectors Last Updated: Monday, 2017-10-30 10:31 Anomaly detectors can be applied to a variety of domains like fraud detection, security, quality control, medicine, etc.
BigML anomaly detectors are built using an unsupervised anomaly detection technique. Therefore, you do not need to explicitly label each instance in your dataset as "normal" or "abnormal".
When you create a new anomaly detector, it automatically returns an anomaly score for the top n most anomalous instances.
The newly created anomaly detector can also be used to later create anomaly scores for new data points or batch anomaly scores for all the instances of a dataset. You can also list all of your anomaly detectors.
This can be used to change the names of the fields in the anomaly detector with respect to the original names in the dataset or to tell BigML that certain fields should be preferred.
Must be a number greater than or equal to 2 and less than or equal to 1000 (or 16 in development mode). All the fields in the dataset Specifies the fields to be considered to create the anomaly detector. The range of successive instances to build the anomaly detector. Example: "MySample" tags optional Array of Strings A list of strings that help classify and index your anomaly detector.
The minimum number is 1 and the maximum is 1024. Example: 256 You can also use curl to customize a new anomaly detector. Read the Section on Sampling Your Dataset to learn how to sample your dataset. Once an anomaly detector has been successfully created it will have the following properties. Creating an anomaly detector 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 anomaly detector goes through a number of states until its fully completed. Through the status field in the anomaly detector you can determine when the anomaly detector has been fully processed and ready to be used to create predictions. Thus when retrieving an anomaly, 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 anomaly detector, you need to PUT an object containing the fields that you want to update to the anomaly detector' s base URL.
Once you delete an anomaly detector, it is permanently deleted. If you try to delete an anomaly detector a second time, or an anomaly detector that does not exist, you will receive a "404 not found" response. However, if you try to delete an anomaly detector that is being used at the moment, then BigML.
To list all the anomaly detectors, you can use the anomaly base URL. By default, only the 20 most recent anomaly detectors will be returned. You can get your list of anomaly detectors directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your anomaly detectors. Associations Last Updated: Monday, 2017-10-30 10:31 Association Discovery is a method to find out relations among values in high-dimensional datasets. It is commonly used for market basket analysis.
For example, finding customer shopping patterns across large transactional datasets like customers who buy hamburgers and ketchup also consume bread, can help businesses to make better decisions on promotions and product placements. Association Discovery can also be used for other purposes such as early detection of failures or incidents, intrusion detection, web mining, or biotechnology.
Note that traditionally association discovery look for co-occurrence and do not consider the order in which an item appear within an itemset. Associations can handle categorical, text and numeric fields as input fields: You can create an association selecting which fields from your dataset you want to use.
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