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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.
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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. Did you know the Titans finished 3rd in rushing behind a rejuvenated DeMarco Murray and rookie Derrick Henry. Did you know tight end Delanie Walker and wide receiver Rishard Matthews combined for 16 touchdowns. Did you know they signed Eric Decker who has scored double-digit touchdowns in three of the last five seasons.
What does all this mean. It means a Mariota vs Winston Super Bowl is forthcoming. Again, lots of value here. He tossed 28 touchdowns against only 2 interceptions, which was good enough to finish second behind Matt Ryan.It specifies the total number of fields, the current offset, and limit, and the number of fields (count) returned. In a future version, you will be able to share sources with other co-workers or, if desired, make them publicly available.
It includes a code, a message, and some extra information. See the table below. This is the date and time in which the source was updated with microsecond precision.
It follows this pattern yyyy-MM-ddThh:mm:ss.
All times are provided in Coordinated Universal Time (UTC). Source Fields The property fields is a dictionary keyed by an auto-generated id per each field in the source. Before a source is successfully created, BigML. The source goes through a number of states until all these analyses are completed. Through the status field in the source you can determine when the source has been fully processed and is ready to be used to create a dataset. Thus when retrieving a source, 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.
In all other respects, the source is the same as the one you would get without any filtering parameter above. To update a source, you need to PUT an object containing the fields that you want to update to the source' s base URL.
Once you delete a source, it is permanently deleted. If you try to delete a source a second time, or a source that does not exist, you will receive a "404 not found" response. However, if you try to delete a source that is being used at the moment, then BigML.
To list all the sources, you can use the source base URL. By default, only the 20 most recent sources will be returned. You can get your list of sources directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your sources. 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.Through the status field in the time series you can determine when time series has been fully processed and ready to be used to create forecasts.
Thus when retrieving a timeseries, 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 time series, you need to PUT an object containing the fields that you want to update to the time series' base URL.
Once you delete a time series, it is permanently deleted. If you try to delete a time series a second time, or a time series that does not exist, you will receive a "404 not found" response.Unify tf1 logo
However, if you try to delete a time series that is being used at the moment, then BigML. To list all the time series, you can use the timeseries base URL. By default, only the 20 most recent time series will be returned.
You can get your list of time series directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your time series. Deepnets Last Updated: Monday, 2017-10-30 10:31 A deepnet in BigML is a supervised learning method to solve classification and regression problems.
Deepnets are an optimized version of Deep Neural Networks, a class of machine-learned models inspired by the neural circuitry of the human brain. In these classifiers, the input features are fed to a group of nodes called a layer.
Then the entire layer transforms an input vector into a new intermediate feature vector. This new vector is fed as input to another layer of nodes. This process continues layer by layer, until we reach the final output layer of nodes, where the output is the network's prediction: an array of per-class probabilities for classification problems or a single, real value for regression problems.
The network architectures supported by BigML can be deep or shallow. The advantage of training deep architectures is that hidden layers have the opportunity to learn higher-level representations of the data that can be used to make correct predictions in cases where a direct mapping between input and output is difficult.
For example, when classifying images of numeric digits, the input layer is raw pixels, the output layer is the probability for each digit, and the intermediate layers may learn features that represent the presence of, say, a loop or a vertical stroke. Deep Neural Networks are notoriously sensitive to the chosen topology and the algorithm used to optimize the parameters thereof. This sensitivity means that hand-tuning the topology and optimization algorithm can be difficult and time-consuming as the number of choices that lead to poor networks typically vastly outnumber the choices that lead to good ones.
To combat this problem, BigML offers first class support for automatic network topology search and parameter optimization. The algorithm BigML uses is a variant on the hyperband algorithm. Instead of selecting candidates for evaluation at random, however, we use an acquisition technique based on techniques from Bayesian parameter optimization. You can also list all of your deepnets. This argument can be used to change the names of the fields in the models of the deepnet with respect to the original names in the dataset.
All the fields in the dataset Specifies the fields to be included as predictors in the models of the deepnet. Example: false name optional String,default is dataset's deepnet The name you want to give to the new deepnet. One per objective class. Each entry is map containing the specific parameters for the algorithm.
See the Optimizer Object for more details. Specifies the type of ordering followed to build the models of the deepnet. The range of successive instances to build the models of the deepnet. The final deepnet returned by the search is a compromise between the top n networks found in the search.Weather in bacau 5 days
Example: true seed optional String A string to be hashed to generate deterministic samples. Dataset sampling doesn't apply to evaluations for time series.Yea (VIC) Non-TAB 3. Auspicious Lad (1) 1.
Bern for You (3) 4. Golden Tart (5) Scratched 5. BERN FOR YOU winner at Healesville and placed once this campaign, include in exotics. GOLDEN TART genuine on pace runner in a race without much early speed and should run fitter for past attempts, outside hope.How to sue for fdcpa violation
RANDAROCK on a seven day back-up and should run fitter for past attempts, for the wider exotics. Ebony Royal (1) 3. Lochend Emmarose (2) 1. Lara Lad (4) EBONY ROYAL resumes after a spell of 26 weeks and expected to settle off the speed, serious player. THEMOOSE resumes from a 15 week spell and should be near the speed in a race without much early pace, include in exotics. LOCHEND EMMAROSE finished in the middle of the pack last start at Woolamai and placed once this prep at Yea, capable of getting into the money with a bit of luck.
Looks the leader and will take plenty of catching, for the wider exotics. Norsika (4) Scratched 2. King Mapoora (7) ScratchedStand-out between the top two picks. NORSIKA all wins have come when faced with dry ground, genuine contender. DEHUGHES coming off a win at Yea and two of four wins have come from dry ground, can figure. FOLD resumes from a 28 week spell and four from five wins have been in the dry, in with a chance. KING MAPOORA has two placings from nine runs this prep and four of five wins have come from dry ground, not without each-way claims.
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Code It (1) 3. Dad 'n' Bri's Shed (8) 5. Salaqua (6) MAGNYTE has good early speed and won once this prep at Yea two runs back, major contender. CODE IT drawn perfectly, place hope. DAD 'N' BRI'S SHED 2 wins from four attempts this campaign and all wins have come when faced with dry ground, quinella.We travelled in July and were very lucky with the weather.
Iceland is one of the most beautiful countries we have ever visited, and we were impressed by the friendliness and hospitality of the people ( and also by the fact that almost everybody spoke English). From pick-up at the airport to the selection of the places to stay, every detail was exactly as advertised and flawless. The documentation we received prior to arrival and then again in Reykjavik was plentiful and very useful.
We are full of praise and shall share our experience with friends and family.
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Thordis: Excellent, excellent, excellent. Everything provided and planned for was fabulous and amazing. Everything provided was perfect!!. My favorite place was the Blue Lagoon. I went there, twice, The massages were awesome!!. I used Nordic Visitor when I travelled to Iceland for the first time, and it was a wonderful experience.
They gave us free passes to the Blue Lagoon for our birthdays, and they also let us change our itinerary after we arrived when we realized we wanted to spend more time with a friend in Reykjavik. This was at no cost to us. The thing I appreciated most was the fact that they chose hostels for us that were difficult to find in guides, and this made our experience so much better.
One hostel we stayed at (Hrifunes) was just a small house on top of a mountain, and I never would have known to stay there, but it was my favorite of all the hostels. I will definitely use Nordic Visitor again in their other locations. I had such a wonderful experience with them.වාහනය සර්විස් කරන්න කලින් ඔබ දැනගත යුකුම දේ.
Nordic Visitor, with Alexandra as our local travel specialist, set up our nine night self-drive tour and thought of everything. Our personal taxi driver met us at Keflavik Int'l Airport after an overnight flight from Boston to hand us our travel documents, local cellphone, and give us a guided tour on our way to our Keflavik hotel.
The documents (map, detailed itinerary, highlights of Iceland on our route, useful information, expanded tourist information guidebook, and daily travel vouchers) directed us around the island with ease and were very professionally put together.
The Nordic Visitor arranged rental SUV (SUV allows access to restricted back-country roads) came with a free GPS. The middle level Comfort Accomodations were quite adequate. Alexandra was always there for us: before the trip to relay details and answer e-mail questions, on arrival in Reykjavik to further recommend sights along our route, during the trip if needed via out free cellphone, and at the end of the trip during the extra day that she arranged for us in Reykjavik.
Everything was well organized and complete. We particularly liked having a cell phone for emergencies etc. We loved that the route and accommodations were arranged for us. This took a lot of stress out of the trip. We loved Iceland and will consider returning as well as using Nordic visitor for future travel to other destinations. Nordic Visitor was one of the best travel decisions I've ever made. They made everything so easy, while giving us the freedom of being able to 'adventure' on our own.
Our itinerary and maps were comprehensive, with many personal touches from Thordis, our travel agent. I hope to book another trip with Nordic Visitor again soon.
Helena was one of the best travel consultants I have ever worked with. She quickly answered all questions and was a delight to work with. We just returned from our trip and we wanted to tell you thank you.
Everything you planned was wonderful. We loved the tours in Kiruna, especially the dog sledding. The ice hotel was amazing and we loved the room you picked for us.
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The hotels you picked were very nice and we never would have found them on our own.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.
The batch centroid goes through a number of states until its finished. Through the status field in the batch centroid you can determine when it has been fully processed.
Once you delete a batch centroid, it is permanently deleted. If you try to delete a batch centroid a second time, or a batch centroid that does not exist, you will receive a "404 not found" response.
However, if you try to delete a batch centroid that is being used at the moment, then BigML. To list all the batch centroids, you can use the batchcentroid base URL. By default, only the 20 most recent batch centroids will be returned. You can get your list of batch centroids directly in your browser using your own username and API key with the following links.
You can also paginate, filter, and order your batch centroids. Batch Anomaly Scores Last Updated: Monday, 2017-10-30 10:31 A batch anomaly score provides an easy way to compute an anomaly score for each instance in a dataset in only one request. Batch anomaly scores are created asynchronously. You can also list all of your batch anomaly scores. You can easily create a new batch anomaly score using curl as follows. Example: true importance optional Boolean,default is false Whether field importance scores are added as additional columns for each input field.
All the fields in the dataset Specifies the fields in the dataset to be considered to create the batch anomaly score. Example: "my new anomaly score" 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".
Example: "Anomaly Score" separator optional Char,default is "," The separator that you want to get between fields in the generated csv file. For example, to create a new batch anomaly score named "my batch anomaly score", that will not include a header, and will only output the field "000001" together with the score for each anomaly score. Once a batch anomaly score has been successfully created it will have the following properties.
Creating a batch anomaly score 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 anomaly score goes through a number of states until its finished. Through the status field in the batch anomaly score you can determine when it has been fully processed. Once you delete a batch anomaly score, it is permanently deleted.
If you try to delete a batch anomaly score a second time, or a batch anomaly score that does not exist, you will receive a "404 not found" response.
However, if you try to delete a batch anomaly score that is being used at the moment, then BigML. To list all the batch anomaly scores, you can use the batchanomalyscore base URL. By default, only the 20 most recent batch anomaly scores will be returned. You can get your list of batch 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 batch anomaly scores. Batch Topic Distributions Last Updated: Monday, 2017-10-30 10:31 A batch topic distribution provides an easy way to compute a topic distribution for each instance in a dataset in only one request. Batch topic distributions are created asynchronously. You can also list all of your batch topic distributions. You can easily create a new batch topic distribution using curl as follows. All the fields in the dataset Specifies the fields in the dataset to be considered to create the batch topic distribution.
Example: "my new batch topic distribution" 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".
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