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

Rubner-Tavan's network


In Chapter 5, EM Algorithm and Applications, we said that any algorithm that decorrelates the input covariance matrix is performing a PCA without dimensionality reduction. Starting from this approach, Rubner, and Tavan (in the paper A Self-Organizing Network for Principal-Components Analysis, Rubner J., Tavan P., Europhysics. Letters, 10(7), 1989) proposed a neural model whose goal is decorrelating the output components to force the consequent decorrelation of the output covariance matrix (in lower-dimensional subspace). Assuming a zero-centered dataset and E[y] = 0, the output covariance matrix for m principal components is as follows:

Hence, it's possible to achieve an approximate decorrelation, forcing the terms yiyj with i ≠ j to become close to zero. The main difference with a standard approach (such as whitening or vanilla PCA) is that this procedure is local, while all the standard methods operate globally, directly with the covariance matrix. The neural model...