Active learning is a special case of machine learning where a model can query a user for input. In this post, we will see how we can use active learning to label large data sets. For most machine learning tasks, large amounts of labeled data is needed is need for model training. However, the process of labeling data can be extremely time consuming and/or expensive. Using active learning, we can leverage a classification model to do most of the labeling for us, so that we only need to label samples when it is most needed.
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A generative adversarial network (GAN) is a system composed of two neural networks: a generator and a discriminator. The discriminator takes a data instance as input, and classifies it as ‘Real’ or ‘Fake’ with respect to a training data set. The generator takes Gaussian noise and transforms it into a synthetic data sample with the goal of fooling the discriminator. The discriminator learns to classify samples as real or fake. The generator learns from errors in failed attempts at fooling the discriminator.
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Exploring how TensorFlow models can be served using TensorFlow Serving…
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