Book Image

Python: Advanced Guide to Artificial Intelligence

By : Giuseppe Bonaccorso, Rajalingappaa Shanmugamani
Book Image

Python: Advanced Guide to Artificial Intelligence

By: Giuseppe Bonaccorso, Rajalingappaa Shanmugamani

Overview of this book

This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: • Mastering Machine Learning Algorithms by Giuseppe Bonaccorso • Mastering TensorFlow 1.x by Armando Fandango • Deep Learning for Computer Vision by Rajalingappaa Shanmugamani
Table of Contents (31 chapters)
Title Page
About Packt
Tensor Processing Units

EM algorithm

The EM algorithm is a generic framework that can be employed in the optimization of many generative models. It was originally proposed in Maximum likelihood from incomplete data via the em algorithmDempster A. P., Laird N. M., Rubin D. B., Journal of the Royal Statistical Society, B, 39(1):1–38, 11/1977, where the authors also proved its convergence at different levels of genericity.

For our purposes, we are going to consider a dataset, X, and a set of latent variables, Z, that we cannot observe. They can be part of the original model or introduced artificially as a trick to simplify the problem. A generative model parameterized with the vector θ has a log-likelihood equal to the following:

Of course, a large log-likelihood implies that the model is able to generate the original distribution with a small error. Therefore, our goal is to find the optimal set of parameters θ that maximizes the marginalized log-likelihood (we need to sum—or integrate out for continuous variables...