Probabilistic Deep Learning with TensorFlow

DL colleagues, learn to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library.. Gain high-demand skills in Probabilistic Neural Networks, Deep Learning, Generative Models, Tensorflow and Probabilistic Programming Language (PRPL). The five training modules equip you in: 1) TensorFlow Distributions: Probabilistic modelling is a powerful and principled approach that provides a framework in which to take account of uncertainty in the data, 2) Probabilistic layers and Bayesian Neural Networks: Accounting for sources of uncertainty is an important aspect of the modelling process, especially for safety-critical applications such as medical diagnoses, 3) Bijectors and Normalising Flows: Normalising flows are a powerful class of generative models, that aim to model the underlying data distribution by transforming a simple base distribution through a series of bijective transformations., 4) Variational Autoencoders: Variational autoencoders are one of the most popular types of likelihood-based generative deep learning models. Two networks are jointly learned: an encoder or inference network, as well as a decoder or generative network, and 5) Capstone Project: Develop probabilistic deep learning models using tools and concepts from the TensorFlow Probability library such as Distribution objects, probabilistic layers, bijectors, and KL divergence optimisation. 

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Much career success, Lawrence E. Wilson - Artificial Intelligence Academy