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
Contributors
Preface
19
Tensor Processing Units
Index

Fuzzy C-means


We have already talked about the difference between hard and soft clustering, comparing K-means with Gaussian mixtures. Another way to address this problem is based on the concept of fuzzy logic, which was proposed for the first time by Lotfi Zadeh in 1965 (for further details, a very good reference is An Introduction to Fuzzy Sets, Pedrycz W., Gomide F., The MIT Press). Classic logic sets are based on the law of excluded middle that, in a clustering scenario, can be expressed by saying that a sample xi can belong only to a single cluster cj. Speaking more generally, if we split our universe into labeled partitions, a hard clustering approach will assign a label to each sample, while a fuzzy (or soft) approach allows managing a membership degree (in Gaussian mixtures, this is an actual probability), wij which expresses how strong the relationship is between sample xi and cluster cj. Contrary to other methods, by employing fuzzy logic it's possible to define asymmetric sets...