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

Independent component analysis

We have seen that the factors extracted by a PCA are decorrelated, but not independent. A classic example is the cocktail party: we have a recording of many overlapped voices and we would like to separate them. Every single voice can be modeled as a random process and it's possible to assume that they are statistically independent (this means that the joint probability can be factorized using the marginal probabilities of each source). Using FA or PCA, we are able to find uncorrelated factors, but there's no way to assess whether they are also independent (normally, they aren't). In this section, we are going to study a model that is able to produce sparse representations (when the dictionary isn't under-complete) with a set of statistically independent components.

Let's assume we have a zero-centered and whitened dataset X sampled from N(0, I) and noiseless linear transformation:

In this case, the prior over, z, is modeled as a product of independent variables...