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

Segmenting instances

While analyzing an image, our interest will only be drawn to certain instances in the image. So, it was compelled to segment these instances from the remainder of the image. This process of separating the required information from the rest is widely known as segmenting instances.  During this process, the input image is first taken, then the bounding box will be localized with the objects and at last, a pixel-wise mask will be predicted for each of the class. For each of the objects, pixel-level accuracy is calculated. There are several algorithms for segmenting instances. One of the recent algorithms is the Mask RCNN algorithm proposed by He at al. ( The following figure portrays the architecture of Mask R-CNN:

Reproduced with permission from He et al.


The architecture looks similar to the R-CNN with an addition of segmentation. It is a multi-stage network with end-to-end training. The region proposals are learned. The network is...