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

Object detection API


Google released pre-trained models with various algorithms trained on the COCO dataset for public use. The API is built on top of TensorFlow and intended for constructing, training, and deploying object detection models. The APIs support both object detection and localization tasks. The availability of pre-trained models enables the fine-tuning of new data and hence making the training faster. These different models have trade-offs between speed and accuracy. 

Installation and setup

Install the Protocol Buffers (protobuf) compiler with the following commands. Create a directory for protobuf and download the library directly:

mkdir protoc_3.3
cd protoc_3.3
wget https://github.com/google/protobuf/releases/download/v3.3.0/protoc-3.3.0-linux-x86_64.zip

Change the permission of the folder and extract the contents, as shown here:

chmod 775 protoc-3.3.0-linux-x86_64.zip
unzip protoc-3.3.0-linux-x86_64.zip

Protocol Buffers (protobuf) is Google's language-neutral, platform-neutral...