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

Factor analysis

Let's suppose we have a Gaussian data generating process, pdata N(0Σ), and M n-dimensional zero-centered samples drawn from it:

If pdata has a mean μ ≠ 0, it's also possible to use this model, but it's necessary to account for this non-null value with slight changes in some formulas. As the zero-centering normally has no drawbacks, it's easier to remove the mean to simplify the model.

One of the most common problems in unsupervised learning is finding a lower dimensional distribution plower such that the Kullback-Leibler divergence with pdata is minimized. When performing a factor analysis (FA), following the original proposal published in EM algorithms for ML factor analysis, Rubin D., Thayer D., Psychometrika, 47/1982, Issue 1, and The EM algorithm for Mixtures of Factor Analyzers, Ghahramani Z., Hinton G. E., CRC-TG-96-1, 05/1996, we start from the assumption to model the generic sample x as a linear combination of Gaussian latent variables, z, (whose dimension p is...