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

Predicting pixels

Image classification is the task of predicting labels or categories. Object detection is the task of predicting a list of several deep learning-based algorithms with its corresponding bounding box. The bounding box may have objects other than the detected object inside it. In some applications, labeling every pixel to a label is important rather than bounding box which may have multiple objects. Semantic segmentation is the task of predicting pixel-wise labels.

Here is an example of an image and its corresponding semantic segmentation:



As shown in the image, an input image is predicted with labels for every pixel. The labels could be the sky, tree, person, mountain, and bridge. Rather than assigning a label to the whole image, labels are assigned to each pixel. Semantic segmentation labels pixels independently. You will notice that every people is not distinguished. All the persons in the image are labeled in the same way.

Here is an example where every instance of the...