Artificial-Vision-and-Language-Processing-for-Robotics

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Artificial-Vision-and-Language-Processing-for-Robotics

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# Artificial Vision and Language Processing for Robotics
Artificial Vision and Language Processing for Robotics begins by discussing the theory behind robots. You will learn the basics of robots and important issues while working with them. Then, you’ll compare different methods used to work with robots and explore computer vision, its algorithms, and limits.
As you progress through the course, you will learn how to control the robot with natural language processing (NLP) commands. You’ll also study Word2Vec and GloVe embedding techniques, non-numeric data, recurrent neural network (RNNs), and their advanced models. You will 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 will study the ROS and build a conversational agent to manage your robot. In the concluding chapters, you will define intents and phrases for the agent and use GloVe to identify user intent. Then, you will integrate your agent with the ROS and convert an image to text and text to speech. You will also learn to build an object recognition system using your webcam; this system will enable your robot to fully understand its surroundings and make proper decisions.
By the end of this course, you will have the skills you need to build a functional application that can integrate with a ROS to extract useful information about your environment.

## What you will learn
By the end of the course, you will be able to:
* Explore the ROS and build a basic robotic system
* Understand the architecture of neural networks
* Identify conversation intents with NLP techniques
* Learn and use the embedding with Word2Vec and GloVe
* Build a basic CNN and improve it using generative models
* Use deep learning to implement artificial intelligence(AI)and object recognition
* Develop a simple object recognition system using CNNs
* Integrate AI with ROS to enable your robot to recognize objects

### Hardware Requirement
For an optimal student experience, we recommend the following hardware configuration:
* **Processor**: 2GHz dual core processor or better.
* **Memory**: 8 GB RAM.
* **Storage**: 5GB available space

### Software Requirement
You’ll also need the following software installed in advance:
* **OS**: Windows 7 SP1 32/64-bit, Windows 8.1 32/64-bit or Windows 10 32/64-bit, Ubuntu 14.04 or later, or macOS Sierra or later
* **Browser**: Google Chrome / Mozilla Firefox (Latest Version)
* **Conda**
* Notepad++/Sublime Text as IDE
* Python 3.4+ (latest is Python 3.7) installed (from https://python.org)
* NLTK (<= 3.4), spaCy (<=2.0.18), gensim (<=3.7.0), numpy (<=1.15.4), sklearn (<=0.20.1), matplotlib (<=3.0.2), opencv (<=4.0.0.21), keras (<=2.2.4) and tensorflow (<=1.5, >=2.0). 
### Download a free PDF

If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost. Simply click on the link to claim your free PDF.
 <a href="https://packt.link/free-ebook/9781838552268">https://packt.link/free-ebook/9781838552268 </a>

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