Automated Machine Learning (AML) - AutoML

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An emerging class of data science toolkit that is finally making machine learning accessible to business subject matter experts. We anticipate that these innovations will mark a new era in data-driven decision support, where business analysts will be able to access and deploy machine learning on their own to analyze hundreds and thousands of dimensions simultaneously. Business analysts at highly competitive organizations will shift from using visualization tools as their only means of analysis, to using them in concert with AML. Data visualization tools will also be used more frequently to communicate model results, and to build task-oriented user interfaces that enable stakeholders to make both operational and strategic decisions based on output of scoring engines. They will also continue to be a more effective means for analysts to perform inverse analysis when one is seeking to identify where relationships in the data do not exist. 'Five Essential Capabilities: Automated Machine Learning' | Gregory Bonnette

H2O Driverless AI automatically performs feature engineering and hyperparameter tuning, and claims to perform as well as Kaggle masters. AmazonML SageMaker supports hyperparameter optimization. Microsoft Azure Machine Learning AutoML automatically sweeps through features, algorithms, and hyperparameters for basic machine learning algorithms; a separate Azure Machine Learning hyperparameter tuning facility allows you to sweep specific hyperparameters for an existing experiment. Google Cloud AutoML implements automatic deep transfer learning (meaning that it starts from an existing Deep Neural Network (DNN) trained on other data) and neural architecture search (meaning that it finds the right combination of extra network layers) for language pair translation, natural language classification, and image classification. Review: Google Cloud AutoML is truly automated machine learning | Martin Heller

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AutoML

New cloud software suite of machine learning tools. It’s based on Google’s state-of-the-art research in image recognition called Neural Architecture Search (NAS). NAS is basically an algorithm that, given your specific dataset, searches for the most optimal neural network to perform a certain task on that dataset. AutoML is then a suite of machine learning tools that will allow one to easily train high-performance deep networks, without requiring the user to have any knowledge of deep learning or AI; all you need is labelled data! Google will use NAS to then find the best network for your specific dataset and task. AutoKeras: The Killer of Google’s AutoML | George Seif - KDnuggets

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Automatic Machine Learning (AML)

Self-Learning


DARTS: Differentiable Architecture Search

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