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TensorFlow 2.0 in Action: Going Beyond Tensor flow foundations

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I found some interesting information on few thing beyond deep NN in the preview pages of your book

Is tensor flow is for creating DNN pipelines, do tensor flow support stats based models to be combined with DNN? what are the advantages? For a newbie, how one should imbibe such facilities?
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TensorFlow was initially designed as a library to implement deep nets. However not TensorFlow has evolved to become an eco-system that supports a model throught its all stages of life.

For example, with TensorFlow probability you can implement probabilistic models like Bayesian neural networks.

Typically when using TensorFlow in industry or to do most research you won't need to combine frequentist deep nets and bayesian deep nets. Bayesian deep nets are mostly used in specific research. For a newbie, it would be much safer to start with typical deep nets (not Bayesien ones).

Hope this helps.
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