1. Deep Probabilistic Modeling (V). Variational inference with deep neural networks.

    How to apply variational inference to probabilistic models containing deep neural networks.

  2. Deep Probabilistic Modeling (IV). Probabilistic models with deep neural networks.

    How deep neural networks can be used to extend the modelling capacities of a probabilistic model.

  3. Deep Probabilistic Modeling (III). Artificial Neural Networks and Computational Graphs

    A brief review of artificial neural networks and computational graphs.

  4. Deep Probabilistic Modeling (II). Conjugate Exponential Family Models

    A description of latent variable models and variational inference in the context of conjugate exponential models.

  5. Deep Probabilistic Modeling (I). A review of traditional inference methods

    Why traditional probabilistic modeling focused on capture linear relationships among random variables.