AI News, MLDB: The Machine Learning Database

MLDB: The Machine Learning Database

At a high level, transfer learning allows us to take a model that was trained on one task and use its learned knowledge on another task.

The task of that challenge was to classify images into a varied set of 1000 classes, like badger, freight car or cheeseburger.

When solving any machine learning problem, one critical step is picking and designing feature extractors.

They are used to take the thing we want to classify, be it an image, a song or a news article, and transform it into a numerical representation, called a feature vector, that can be given to a classifier.

However, the layers that come before are transformations over the raw image learned by the network because they were the most useful to solve the image classification task.

So the idea will be to run images through the network, but instead of getting the output of the last layer, that is specialised to the ImageNet task, getting the second to last, which will give us a conceptual numerical representation of the images.

So you can think of the Inception model as a way to get from an image to a feature vector over which a new classifier can efficiently operate.

We are leveraging hundreds of hours of GPU compute-­time that went into training the Inception model, but applying it to a completely new task.

We made a simple PUT call using pymldb to create the ​inception function, of type tensorflow.graph.

The function loads a trained instance of the Inception model (note that MLDB can transparently load remote resources, as well as files inside of compressed archives;

We specify that the input to the model will be the remote resource located at url, and the output will be the ​pool_3 layer of the model, which is the second to last layer.

Using the pool_3 layer will give us high level features, while the last layer called softmax is the one that is specialized to the ImageNet task.

The {* EXCLUDING(label)} is a good example of MLDB’s row expression syntax that is meant to work with sparse datasets with millions of columns.

When we trained our classifier above, the training procedure created a car_brand_cls_scorer_0 automatically, available in the usual SQL/Rest, that will run the model.

The Machine Learning Database solves machine learning problems end­-to-­end, from data collection to production deployment, and offers world­-class performance yielding potentially dramatic increases in ROI when compared to other machine learning platforms.

François has been applying machine learning for almost 10 years to solve varied problems, like real­-time bidding algorithms and behavioral modelling for the adtech industry, automatic bully detection on web forums, audio similarity and fingerprinting, steerable music recommendation and playlist generation.

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