November 2018
Intermediate to advanced
322 pages
7h 54m
English
Content preview from TensorFlow Machine Learning Projects
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Understanding TensorFlow probability, variational inference, and Monte Carlo methods
TensorFlow Probability (tfp in code – https://www.tensorflow.org/probability/overview#layer_2_model_building) was recently released by Google to perform probabilistic reasoning in a scalable manner. It provides tools and functionalities to define distributions, build neural networks with prior on weights, and perform probabilistic inference tasks such as Monte Carlo or Variational Inference.
Let's take a look at some of the functions/utilities we will be using for building our model:
- Tfp.distributions.categorical: This is a standard categorical distribution that's characterized by probabilities or log-probabilities over K classes. In this project, we have ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.