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


In this chapter, we have introduced the most important label propagation techniques. In particular, we have seen how to build a dataset graph based on a weighting kernel, and how to use the geometric information provided by unlabeled samples to determine the most likely class. The basic approach works by iterating the multiplication of the label vector times the weight matrix until a stable point is reached and we have proven that, under simple assumptions, it is always possible.

Another approach, implemented by Scikit-Learn, is based on the transition probability from a state (represented by a sample) to another one, until the convergence to a labeled point. The probability matrix is obtained using a normalized weight matrix to encourage transitions associated to close points and discourage all the long jumps. The main drawback of these two methods is the hard-clamping of labeled samples; this constraint can be useful if we trust our dataset, but it can be a limitation in the presence...