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

Human face analysis

The human face can be analyzed in multiple ways using computer vision. There are several factors that are to be considered for this, which are listed here:

  • Face detection: Finding the bounding box of location of faces
  • Facial landmark detection: Finding the spatial points of facial features such as nose, mouth and so on
  • Face alignment: Transforming the face into a frontal face for further analysis
  • Attribute recognition: Finding attributes such as gender, smiling and so on
  • Emotion analysis: Analysing the emotions of persons
  • Face verification: Finding whether two images belong to the same person
  • Face recognition: Finding an identity for the face
  • Face clustering: Grouping the faces of the same person together 

Let's learn about the datasets and implementation of these tasks in detail, in the following sections.

Face detection

Face detection is similar to the object detection, that we discussed in Chapter 23, Object Detection. The locations of the faces have to be detected from the...