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

Datasets


The PASCAL and COCO datasets that were mentioned in Chapter 23, Object Detection, can be used for the segmentation task as well. The annotations are different as they are labelled pixel-wise. New algorithms are usually benchmarked against the COCO dataset. COCO also has stuff datasets such as grass, wall, and sky.  The pixel accuracy property can be used as a metric for evaluating algorithms.

Note

Apart from those mentioned, there are several other datasets in the areas of medical imaging and satellite imagery. The links to a few of them are provided here for your reference:

 

  • http://www.cs.bu.edu/~betke/BiomedicalImageSegmentation
  • https://www.kaggle.com/c/intel-mobileodt-cervical-cancer-screening/data
  • https://www.kaggle.com/c/diabetic-retinopathy-detection
  • https://grand-challenge.org/all_challenges
  • http://www.via.cornell.edu/databases
  • https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection
  • https://aws.amazon.com/public-datasets/spacenet
  • https://www.iarpa.gov/challenges/fmow.html...