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

Detecting objects

There are several variants of object detection algorithms. A few algorithms that come with the object detection API are discussed here.

Regions of the convolutional neural network (R-CNN)

The first work in this series was regions for CNNs proposed by Girshick et al.( . It proposes a few boxes and checks whether any of the boxes correspond to the ground truth. Selective search was used for these region proposals. Selective search proposes the regions by grouping the color/texture of windows of various sizes. The selective search looks for blob-like structures. It starts with a pixel and produces a blob at a higher scale. It produces around 2,000 region proposals. This region proposal is less when compared to all the sliding windows possible. 

The proposals are resized and passed through a standard CNN architecture such as Alexnet/VGG/Inception/ResNet. The last layer of the CNN is trained with an SVM identifying the object with a no-object...