This lecture describes modular ways of formulating and learning distributed representations of data. The objective is for you to learn: How to specify models such as logistic regression in layers; how to formulate layers and loss criterions; how well formulated local rules results in correct global rules; how back-propagation works; how this manifests itself in Torch.