There are no rules for names beyond those technical requirements, but here are some best-practices: Model names should be descriptive and distinctiveyou may need to pick them out of lists of many names in logs or reports.
Version names are best kept short and simple. Model and version limits The Cloud ML Engine quota policy sets a limit of 100 models per project and limits the total number of versions (combined between all models) to 200. Model deployment parameters Cloud ML Engine needs some information to create your model version. Version name A name for the new version that is unique among the names of other versions of the model.
Description You can provide a description for your version. Deployment URI You must provide the URI of the Cloud Storage location where your SavedModel is stored. Cloud ML Engine pulls the model from this location and deploys it. This parameter is called --origin in the gcloud ml-engine versions create command. Runtime version Cloud ML Engine uses the latest stable runtime version to deploy your model version unless you specify a different supported one.
The runtime version primarily determines the version of TensorFlow that the prediction service uses to run your model. When you run a batch prediction job you have the option of overriding the assigned runtime version. Online prediction always uses the runtime version set when the model version is deployed. Manual scaling You can specify the number of training nodes to keep running for your model version.
Warning: When you manually set the number of nodes to keep ready for your version, those nodes are considered to be constantly in use, even when not serving predictions. This means that you are charged the hourly rate for each node from the moment you create the version until you delete it. You can't change this value without deploying your model to a different version. Staging bucket If you are using the gcloud command-line tool to deploy your model, you can use a SavedModel on your local computer.
Graph changes for prediction You may have included TensorFlow Ops in your computation graph that were useful primarily in the context of training. Getting predictions You can send new data to your deployed model versions to get predictions. Online prediction versus batch prediction Cloud ML Engine provides two ways to get predictions from trained models: online prediction (sometimes called HTTP prediction), and batch prediction. The differences are shown in the following table: Online prediction Batch prediction Optimized to minimize the latency of serving predictions.
Optimized to handle a high volume of instances in a job and to run more complex models. Can process one or more instances per request. Predictions returned in the response message. Predictions written to output files in a Cloud Storage location that you specify. Input data passed directly as a JSON string. Input data passed indirectly as one or more URIs of files in Cloud Storage locations. Returns as soon as possible. Anyone with Viewer access to the project can request.
Must be a project Editor to run. Runs on the runtime version and in the region selected when you deploy the model. Can run in any available region, using any available runtime version. Though you should run with the defaults for deployed model versions. Runs models deployed to Cloud ML Engine.
Runs models deployed to Cloud ML Engine or models stored in accessible Google Cloud Storage locations.
The needs of your application dictate the type of prediction you should use. Batch prediction latency If you use a simple model and a small set of input instances, you'll find that there is a considerable difference between how long it takes to finish identical prediction requests using online versus batch prediction. Understanding prediction nodes and resource allocation Cloud ML Engine measures the amount of processing you consume for prediction in node hours.Over time, the Ad Exchanges and RTBs will simplify the custom RTB setups for publishers.
Currently, most of the providers offer long setups requiring a decent level of developer resources to learn and implement correctly. The setups can be fairly complex on the DFP end as well when going through for the first time. These setups will be become more automated, the instructions will improve (ie. Later in 2015 aggregated custom RTBs should come into the picture as well, even though most current custom RTBs claim to target the most exchanges and DSPs under the moon.
This will be improved by Google. Setting up video ads via DFP Small Business is possible, but very cumbersome. Including video ad setup in Publisher University was a great step towards this. Expect unique video content to become more mainstream and a larger percentage of videos to be monetized as publishers realize how lucrative they are and as the setups become simpler. Related Read: How to Monetize Adsense for VideosPeople have been waiting for this for a very long time.Lenovo tpm bitlocker
Much of this translates into the Google Display Network via contextual targeting. Google has done this so well, and has improved on it every year while building unparalleled advertiser confidence.
Most publishers would jump at the opportunity to monetize their display traffic with another big name player. Facebook is very close to being able to run a display ad network for external publishers and leverage the data Facebook already has from following the browsing experience of each of their users. A large percentage of publishers already have the Facebook widget pinned to their sidebar.
These users are already being targeted via other DSPs, exchanges and ad networks. Google has a good idea what Facebook is scheming and no doubt is making plans of their own. Google is also feeling the push from new trending revenue streams like content discovery ads, native ads and mobile interstitials.
Google will release new revenue streams available to publishers. They will do this in typical Google fashion. Offer the beta to their large and most trusted premium publishers to refine, then offer it to some vendor partners followed by launching it in full as a new ad tag for AdSense or automatically integrate it into the Ad Exchange demand.
Google should also be cooking up some innovative revenue streams of their own which would be released in 2015 as long as the betas go successfully.You can also paginate, filter, and order your clusters. 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.That means I am happy. What an amazing app, the after ship team has really outdone themselves this time. I highly recommend this app. Get your customers out of the dark and keep them updated. It is very powerful and helpful app. I really recomend to use it. Increase the productivity and efficiency. Super easy to use and track returns with this - it integrates perfectly with ShopifyI think its a great app that helps me to easily offer return options www.
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The tour and preperation exceeds our expectation. For example the tour package included sun glasses for the solar eclipse which happened during our tour. Thanks for the wonderful experience Iceland. We will come back for sure. Our trip gave us plenty to do while still leaving us time to relax and explore the city at our own pace. It was so nice to not have to worry about planning every day-- we just showed up where we were supposed to be at the time we were given and everything else was taken care of.
All of our tours and excursions were so much fun. We had great service everywhere we went. Iceland is a beautiful country and everyone that we met was so friendly and helpful. People were constantly going out of their way to help us. We loved every minute of our trip and we were so sad to leave. I would also like to thank Gudrun for the fabulous job she did planning our tour.
My husband and I are very careful travelers so we were constantly emailing her with questions and requests. She handled them all beautifully and was always prompt to return an answer. We had one small hiccup with our driver during the big storm and she called me to tell me she had already arranged for someone else to drive us. She even helped us plan our wedding which would not have happened without her. She coordinated the florist, the hairstylist and even the pastor.
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I will share our trip with everyone and encourage them to use your company to book their travel. When we travel to the North again we will definitely use Nordic Visitor.The accommodation was varied and very friendly. The hand- annotated road map was especially useful. We saw things which we would not have found ourselves because they had been highlighted on the map or in the itinerary. An especial highlight was the haycart puffin tour at Ingolshofthi.
Car hire was also an efficient and quick affair, much better than our last car-hire experience in France. We had not realised just how large Iceland is and we would love to return to see the rest of the country. The overall organization was very well executed. We had our reservations made everywhere we went, with pre-booked departures on the tours we wanted to take. This eliminated the deception of showing up at the counter and being told that the tour is full and not able to register.
It allowed us to benefit from every hour we were in Iceland. The small car was in good condition (Ford Focus). Great service, great suggestions. The map outlined and prepared for us was great, along with ideas and suggestions for each day. It was just enough. Thank you VERY MUCH. Guides on our tours were informative, interesting, and very easy to talk to with good senses of humor.
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The trip and services were outstanding. We loved Iceland and found the Icelanders to be extremely friendly people. I want to thank Cecilia, she was wonderful. The trip was amazing, we had no problems while travelling and the hotel selection was good. She did a great job. The staff at the Oslo Clarion Royal Christiana were amazing. My husband got sick during the trip and they went out of the way to make sure he felt better. Great choice of hotel. I wanted to go to Iceland and was going by myself.
Figured I'd book a tour, seemed easiest. Found Nordic Visitor and thought, "looks good. Nordic Visitor did everything. Booked my airport transfers, booked my hotels, provided tour guide, everything.
I literally just had to show up. The tour group was nice and small, perfect. The tour guide was super knowledgeable and would provide lots of little tidbits of info. Part of the tour was to try and see the Northern Lights.
Unfortunately, the weather was not cooperating the days we were on the tour. Well, on the last day, it was finally clear, so the tour guide called the office and got us all booked on a Northern Lights tour for that night. We saw the most amazing Northern Lights. And it was all because the tour guide went the extra mile and got us on the tour.The content of this reprint is for informational purposes only and NOT a substitute for professional advice, diagnosis, or treatment. Effects of smartphone addiction Signs and symptoms of smartphone addiction Are you addicted to your smartphone.
Self-help tips for smartphone addiction Treatment for smartphone addiction Helping a child or teen with smartphone addiction Related articles Topic PageAddictions Smartphone Addiction Tips for Breaking Free of Compulsive Smartphone Use While a smartphone, tablet, or computer can be a hugely productive tool, compulsive use of these devices can interfere with your daily life, work, and relationships.
What is smartphone addiction. Smartphone addiction can encompass a variety of impulse-control problems, including: Virtual relationships. Cybersex addiction Compulsive use of Internet pornography, sexting, nude-swapping, adult chat rooms, or messaging services can impact negatively on your real-life intimate relationships and overall emotional health.Raidforums database index
Withdrawal symptoms from smartphone addiction A common warning sign of smartphone or Internet addiction is experiencing withdrawal symptoms when you try to cut back on your smartphone use.
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Yes No Please answer all the questions Score: Interpreting the score: 4 or more: You might benefit by examining how much time you spend on your smartphone and consider changing your use patterns.How do i find my otp code
This questionnaire is not intended to replace professional diagnosis. We depend on support from our readers. All donations help and are greatly appreciated. Yes No Yes No Yes No Yes No Yes No 6. Yes No Yes No 9. Yes No Yes No 11.
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