Trainable probability distributions with Tensorflow

In the previous post, we fit a Gaussian curve to data with maximum likelihood estimation (MLE). For that, we subclassed tf.keras.layers.Layer and wrapped up the model’s parameters in our custom layer. Then, we used negative log-likelihood minimization to have Tensorflow figure out the optimal values for the distribution’s parameters. In today’s short post, we will again fit a Gaussian curve to normally distributed data with MLE. However, we will use Tensorflow’s trainable probability distributio...

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