Book Image

Artificial Vision and Language Processing for Robotics

By : Álvaro Morena Alberola, Gonzalo Molina Gallego, Unai Garay Maestre
Book Image

Artificial Vision and Language Processing for Robotics

By: Álvaro Morena Alberola, Gonzalo Molina Gallego, Unai Garay Maestre

Overview of this book

Artificial Vision and Language Processing for Robotics begins by discussing the theory behind robots. You'll compare different methods used to work with robots and explore computer vision, its algorithms, and limits. You'll then learn how to control the robot with natural language processing commands. You'll study Word2Vec and GloVe embedding techniques, non-numeric data, recurrent neural network (RNNs), and their advanced models. You'll create a simple Word2Vec model with Keras, as well as build a convolutional neural network (CNN) and improve it with data augmentation and transfer learning. You'll study the ROS and build a conversational agent to manage your robot. You'll also integrate your agent with the ROS and convert an image to text and text to speech. You'll learn to build an object recognition system using a video. By the end of this book, you'll have the skills you need to build a functional application that can integrate with a ROS to extract useful information about your environment.
Table of Contents (12 chapters)
Artificial Vision and Language Processing for Robotics
Preface

Improving Your Model - Data Augmentation


There are situations, at times, where you would not be able to improve the accuracy of your model by building a better model. Sometimes, the problem is not the model but the data. One of the most important things to consider when working with machine learning is that the data you work with has to be good enough for a potential model to generalize that data.

Data can represent real-life things, but it can also include incorrect data that may perform badly. This can happen when you have incomplete data or data that does not represent the classes well. For those cases, data augmentation has become one of the most popular approaches.

Data augmentation actually increases the number of samples of the original dataset. For computer vision, this could mean increasing the number of images in a dataset. There are several data augmentation techniques, and you may want to use a specific technique, depending on the dataset. Some of these techniques are mentioned...