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

Bayesian networks

A Bayesian network is a probabilistic model represented by a direct acyclic graph G = {V, E}, where the vertices are random variables Xi, and the edges determine a conditional dependence among them. In the following diagram, there's an example of simple Bayesian networks with four variables:

Example of Bayesian network

The variable x4 is dependent on x3, which is dependent on x1 and x2. To describe the network, we need the marginal probabilities P(x1) and P(x2) and the conditional probabilities P(x3|x1,x2) and P(x4|x3). In fact, using the chain rule, we can derive the full joint probability as:

The previous expression shows an important concept: as the graph is direct and acyclic, each variable is conditionally independent of all other variables that are not successors given its predecessors. To formalize this concept, we can define the function Predecessors(xi), which returns the set of nodes that influence xi directly, for example, Predecessors(x3) = {x1,x2} (we are using...