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
Contributors
Preface
19
Tensor Processing Units
Index

Ultra-nerve segmentation


The Kaggler is an organization that conducts competitions on predictive modelling and analytics. The Kagglers were once challenged to segment nerve structures from ultrasound images of the neck. The data regarding the same can be downloaded from https://www.kaggle.com/c/ultrasound-nerve-segmentation. The UNET model proposed by Ronneberger et al. (https://arxiv.org/pdf/1505.04597.pdf) resembles an autoencoder but with convolutions instead of a fully connected layer. There is an encoding part with the convolution of decreasing dimensions and a decoder part with increasing dimensions as shown here:

Figure illustrating the architecture of the UNET model [Reproduced with permission from Ronneberger et al.]

 

The convolutions of the similar sized encoder and decoder part are learning by skip connections. The output of the model is a mask that ranges between 0 and 1. Let's start by importing the functions, with the help of the following code:

import os
from skimage.transform...