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Marion Hampson, Canada Iceland Full Circle, May 2017 Greg Boyden, United States Iceland Full Circle, May 2017 I am definitely letting my friends know how wonderful your company is and how it makes your stay in Iceland that much more enjoyable. Chris Borczon, United States Golden Circle and South Coast, May 2017 This tour is a perfect tour for a 'taste' of Iceland.
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I even got positive accolade on my revised problem statement from the ARB reviewer. I will definitely be in touch once my data collection is complete and Chapter 4 is ready for construction. Your support, guidance and expertise were just exceptional throughout my entire doctoral venture. You are true professionals.Once you delete a project, it is permanently deleted. If you try to delete a project a second time, or a project that does not exist, you will receive a "404 not found" response.
However, if you try to delete a project that is being used at the moment, then BigML. To list all the projects, you can use the project base URL. By default, only the 20 most recent projects will be returned. You can get your list of projects directly in your browser using your own username and API key with the following links.
You can also paginate, filter, and order your projects. Sources Last Updated: Monday, 2017-10-30 10:31 A source is the raw data that you want to use to create a predictive model. A source is usually a (big) file in a comma separated values (CSV) format. See the example below. Each row represents an instance (or example). Each column in the file represents a feature or field.
The last column usually represents the class or objective field. The file might have a first row named header with a name for each field. See below for more details. You can also list all of your sources. The first dictionary defines the keys that will be selected.
To create a new source, you need to POST the file containing your data to the source base URL. The file must be attached in the post as a file upload. This allows you to upload binary files in compressed format (. You can easily do this using curl. The option -H lets curl set the content type header while the option -X sets the http method.
You will get the access token and the refresh token. Google Drive example:Select the option to create source from Google Drive: Allow BigML access to your Google Drive: Get the access token and refresh token: After complete these steps you need to POST to the source endpoint URL an object containing at least the file ID (for Google Drive) or the bucket and the file name (for Google Storage) and the access token.
Including also the refresh token is optional before your access token expires. Including it avoids you to be worried about expiration time. You first need to authorize BigML access from your own Google Apps application. After the authorization process you will get your access token and refresh token from the Google Authorization Server. Then the process is the same as creating a remote source using BigML application described above.
You need to POST to the source endpoint an object containing at least the file ID (for Google Drive) or the bucket and the file name (for Google Storage) and the access token, but in this case you will also need to include the app secret and app client from your App. Again, including the refresh token is optional. Your values for app client and app secret appear as Client secret and Client ID in Google developers console respectively. This way is specially useful if you want to model small amounts of data generated by an application.
It can be compressed, gzipped, or zipped. You can also use curl to customize your new source with a name and different parser. For example, to create a new source named "my source", without a header and with "x" as the only missing token.
However, if you do specify it, BigML.Let the daily horoscope prediction guide you. Though you are feeling somewhat overwhelmed by responsibilities, there will be no reprieve in sight. Your efforts may be rewarded in terms of cash. You will be inclined towar.
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Forgot Password Create a New Account Keep Me Signed In What does "Remember Me" do. Forgot Password Create a New AccountYou are being directed to ZacksTrade, a division of LBMZ Securities and licensed broker-dealer. The web link between the two companies is not a solicitation or offer to invest in a particular security or type of security.
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This dedication to giving investors a trading advantage led to the creation of our proven Zacks Rank stock-rating system.Not faceplanting in the spotlight is the key. Bottom line: there are more attractive teams with much more value. Similar to Green Bay in that they look the part and are more than capable of playing the part, but something feels askew.
Or a questionable secondary that was easily shredded by Brady in the AFC Championship. Joe Haden should make a difference, although the fact that the Browns of all teams dumped him is worrisome. Not seeing the value here. Even delusional Cowboys fans are beginning to tiptoe off the bandwagon.
Multiple players suspended for portions of the season. Young quarterback primed for a sophomore slump.
Recovering from a collapse as monumental as the one Atlanta suffered last February could take years, not months. The Silver and Black are the trendiest of trendy picks to dethrone the Patriots in the AFC and make a triumphant return to the Super Bowl their first since 2003. For that to happen, Derek Carr must take another step toward elite status, and a rising defense led by reigning Defensive Player of the Year Khalil Mack must plug the leaks that plagued them in 2016.
However, a 12-4 record has raised expectations to meteoric levels. And with that comes increased pressure. But at this price. Is Trevor Siemian still the starting quarterback. Then stay away, even at this price. Dynamic weapons in the passing game.
Two-time Super Bowl winner at quarterback. On paper, Big Blue looks like a good bet. Those two titles Eli won were behind power rushing attacks. Intriguing team with a hellish defense coming off a 12-4 AFC West title.
Trusting either Tom Savage or rookie Deshaun Watson to lead the Texans to the promised land is like tossing money into a fire pit. Not that long ago the Cats were 17-1 and representing the NFC in the Super Bowl. Cam Newton coming off a dreadful 2016 is eager to prove last year was a fluke. Toss in rookie Swiss Army Knife Christian McCaffery and a stout front seven, and there should be a significant level of interest. Especially at this price.
If Atlanta regresses, look out. Young, talented team with a gunslinger at quarterback, a pair of playmaking receivers, and an attacking defense.
The last two NFC champs came from the AFC South. Punting on either of these teams is worth considering at this price. Drew Brees knows time is running out on making another Super Bowl run. Perhaps the addition of Adrian Peterson will give the Saints the boost they need to overcome obvious defensive flaws.
As for the Birds, nobody ever wins the NFC East in consecutive years, and the hype surrounding Carson Wentz seems to be legit. If the Eagle defense can elevate to Top 10 level, it might be enough to carry them into January. Did you know prior to breaking his fibula in Week 16 last season, second-year quarterback Marcus Mariota had 26 touchdowns against only 9 interceptions.Through the status field in the ensemble you can determine when the ensemble has been fully processed and ready to be used to create predictions.
Once you delete an ensemble, it is permanently deleted. If you try to delete an ensemble a second time, or an ensemble that does not exist, you will receive a "404 not found" response.
However, if you try to delete an ensemble that is being used at the moment, then BigML. To list all the ensembles, you can use the ensemble base URL.
By default, only the 20 most recent ensembles will be returned. You can get your list of ensembles directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your ensembles. Logistic Regressions Last Updated: Monday, 2017-10-30 10:31 A logistic regression is a supervised machine learning method for solving classification problems.
You can create a logistic regression selecting which fields from your dataset you want to use as input fields (or predictors) and which categorical field you want to predict, the objective field. Logistic regression seeks to learn the coefficient values b0, b1, b2. Xk must be numeric values. To adapt this model to all the datatypes that BigML supports, we apply the following transformations to the inputs:BigML. You can also list all of your logistic regressions. Value is a map between field identifiers and a coding scheme for that field.
See the Coding Categorical Fields for more details. If not specified, one numeric variable is created per categorical value, plus one for missing values.
This can be used to change the names of the fields in the logistic regression with respect to the original names in the dataset or to tell BigML that certain fields should be preferred.
All the fields in the dataset Specifies the fields to be included as predictors in the logistic regression. If false, these predictors are not created, and rows containing missing numeric values are dropped. Example: false name optional String,default is dataset's name The name you want to give to the new logistic regression. Example: "my new logistic regression" normalize optional Boolean,default is false Whether to normalize feature vectors in training and predicting.
The type of the field must be categorical. The type of the fields must be categorical. The range of successive instances to build the logistic regression. Regularizing with respect to the l1 norm causes more coefficients to be zero, using the l2 norm forces the magnitudes of all coefficients towards zero. Example: "l1" replacement optional Boolean,default is false Whether sampling should be performed with or without replacement. The minimum between that number and the total number of input rows will be used.
Example: 1000 tags optional Array of Strings A list of strings that help classify and index your logistic regression. By default, they are "one-hot" coded. That is, one numeric variable is created per categorical value, plus one for missing values. For a given instance, the variable corresponding to the instance's categorical value has its value set to 1, while the other variables are set to 0.
Using the iris dataset as an example, we can express this coding scheme as the following table:The parameter value is an array where each element is a map describing the coding scheme to apply to a particular field, and containing the following keys:The value for coding determines which of the following methods is used to code the field: If multiple coding schemes are listed for a single field, then the coding closest to the end of the list is used.
Codings given for non-categorical variables are ignored. The dummy class will be the first by alphabetical order.
This is because the default one-hot encoding produces collinearity effects which result in an ill-formed covariance matrix. You can also use curl to customize a new logistic regression. Once a logistic regression has been successfully created it will have the following properties. The coefficients output field is an array of pairs, one pair per class.MONSOON CHARLIE in the money last start running third at Wagga Wagga on a soft track and returns to shorter trip, in with a chance.
PAY THE ONES surprised punters to win at long odds last start at Wagga Wagga and returns to shorter trip, could upset. Paris Sizzler (4) 1.
Kappy Cino (8) 8. Emerald Ice (7) PARIS SIZZLER a winner at first outing this prep and ran six lengths back from the winner last start at Goulburn, key chance. KAPOVER was a winner last start at long odds to break maiden at Wodonga and has good early speed, consider in exotics.
KAPPY CINO tends to go well in the wet and won once this prep at Wagga Wagga seven runs back, for the exotics. EMERALD ICE did nothing to threaten last start at Gundagai on a soft track and expected to settle off the speed, include in exotics. Dangan Ressha (8) 9. Paradiso Girl (2) 11. Upper House (5) Anything could surprise in this below standard field.
DANGAN RESSHA narrowly beaten at long odds at only start at Moruya, hard to go past. PARADISO GIRL amongst the placegetters last start running third at Wagga Wagga and has two placings from five runs this prep, each-way claims. TALICO placed once this prep at Leeton and ran seventh last start at Wagga Wagga, the real danger in the race. UPPER HOUSE on a seven day back-up and ran four lengths back from the winner last start at Sapphire Coast, still in this.
French Politician (11) 1. Fox Beat (1) 11. Magic Word (5) Hard to split the top two picks. FOX BEAT narrowly beaten at long odds last start at Wagga Wagga when first up and drawn ideally, has solid claims. MAGIC WORD back from 31 week spell and generally strong first-up placing at Corowa last attempt, still in this. Makahu Boy (15) Scratched 3.
Comrade Ned (10) 1. Pacific Tycoon (7) Scratched 8. Ticked Off (11) MAKAHU BOY winner despite being unwanted in the betting at only start at Wodonga, well placed.
COMRADE NED last start winner at Yarra Valley on a soft track and drops 1.
PACIFIC TYCOON faded from front position to finish just off the winner last start at Albury and has three placings from three runs this prep, still in this.
TICKED OFF has the speed to overcome a very wide draw, looks threatening. Supply Money (2) 5. Cha Cha King (4) 1.
Loyal Toast (10) SUPPLY MONEY coming off a win at Wodonga and two wins from seven attempts this campaign, hard to go past. CHA CHA KING at home in the wet conditions and has outstanding form at this track, cannot be ruled out. DURE goes well in the wet and drawn ideally, don't treat lightly. LOYAL TOAST in strong form with two wins from 11 attempts this campaign and goes down in weight, capable of getting into the money. Wanna Get a What (4) 4.
Dolly's Due (11) 3. Miss Liffey (7) WANNA GET A WHAT a winner at first outing this prep and has shown early speed in races to date, key chance. DOLLY'S DUE has the speed to overcome a very wide draw, place claims. Proven second-up runner winning in two of six attempts and ran second at Sapphire Coast last try second-up, capable of getting into the money. MISS LIFFEY coming off a win at Wagga Wagga when fresh and likely to race on the speed, strong place chance.
Just Favulous (1) 1.