Summary

Here we discussed some of the mathematical background as well as some implementations we did not cover in the other chapters. First, we discussed the mathematical notation for scalars, vectors, matrices, and tensors. Then, we discussed various operations performed on these data structures such as matrix multiplication and inversion. After that, we discussed various terminology that is useful for understanding probabilistic machine learning, such as probability density functions, joint probability, marginal probability, and Bayes’ rule. Finally, we ended the appendix with a guide to visualizing word embeddings using TensorBoard, a visualization platform that comes with TensorFlow.