Funny akinator characters

Funny akinator characters

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But when the experience is negative, companies need to respond quickly. This needs to be done for the customer who left the review, but perhaps more importantly, for everyone else reading the reviews after. It is always a great feeling to read and hear positive reviews of your company, but negative reviews should be considered an opportunity for the business to do better. Also see here: How To Respond To Positive And Negative Reviews 1.

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Star rating is the number one factor used by consumers to judge a business tweet 4.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. Node allocation for batch prediction The batch prediction service scales the number of nodes it uses to minimize the amount of elapsed time your job takes.

To do that, the service: Allocates some nodes to handle your job when you start it.The parameters for this field are as follows: Creating a time series 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 time series goes through a number of states until its fully completed. 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. 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.

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