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

Deep convolutional networks

As the fully-connected layers are horizontal, the images, which in general are three-dimensional structures (width × height × channels), must be flattened and transformed into one-dimensional arrays where the geometric properties are definitively lost. With more complex datasets, where the distinction between classes depends on more details and on their relationships, this approach can yield moderate accuracies, but it can never reach the precision required by production-ready applications.

The conjunction of neuroscientific studies and image processing techniques suggested experimenting with neural networks where the first layers work with bidimensional structures (without the channels), trying to extract a hierarchy of features that are strictly dependent on the geometric properties of the image. In fact, as confirmed by neuroscientific research about the visual cortex, a human being doesn't decode an image directly. The process is sequential and starts by detecting...