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Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.
Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.
Hierarchical Attentive Recurrent Tracking
Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.
A Simple Example for Imitation Learning with Dataset Aggregation (DAGGER) on Torcs Env