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


When we discussed the Gaussian mixture algorithm, we defined it as Soft K-means. The reason is that each cluster was represented by three elements: mean, variance, and weight. Each sample always belongs to all clusters with a probability provided by the Gaussian distributions. This approach can be very useful when it's possible to manage the probabilities as weights, but in many other situations, it's preferable to determine a single cluster per sample. Such an approach is called hard clustering and K-means can be considered the hard version of a Gaussian mixture. In fact, when all variances Σi → 0, the distributions degenerate to Dirac's Deltas, which represent perfect spikes centered at a specific point. In this scenario, the only possibility to determine the most appropriate cluster is to find the shortest distance between a sample point and all the centers (from now on, we are going to call them centroids). This approach is also based on an important double principle that should...