Book Image

Hands-On Artificial Intelligence for Beginners

By : Patrick D. Smith, David Dindi
Book Image

Hands-On Artificial Intelligence for Beginners

By: Patrick D. Smith, David Dindi

Overview of this book

Virtual Assistants, such as Alexa and Siri, process our requests, Google's cars have started to read addresses, and Amazon's prices and Netflix's recommended videos are decided by AI. Artificial Intelligence is one of the most exciting technologies and is becoming increasingly significant in the modern world. Hands-On Artificial Intelligence for Beginners will teach you what Artificial Intelligence is and how to design and build intelligent applications. This book will teach you to harness packages such as TensorFlow in order to create powerful AI systems. You will begin with reviewing the recent changes in AI and learning how artificial neural networks (ANNs) have enabled more intelligent AI. You'll explore feedforward, recurrent, convolutional, and generative neural networks (FFNNs, RNNs, CNNs, and GNNs), as well as reinforcement learning methods. In the concluding chapters, you'll learn how to implement these methods for a variety of tasks, such as generating text for chatbots, and playing board and video games. By the end of this book, you will be able to understand exactly what you need to consider when optimizing ANNs and how to deploy and maintain AI applications.
Table of Contents (15 chapters)

Setting up your environment

We'll be utilizing the gym environment from OpenAI that we learned about in Chapter 8, Reinforcement Learning, to create an intelligent robotic arm. OpenAI created a virtual environment based on Fetch Robotic Arms, which created the first fully virtualized test space for robotics algorithms:

You should have these environments already installed on your computer from when we installed gym in Chapter 11, Deep Learning for Finance. We'll just need to add two more packages to get this robotics environment up and running:

brew install cmake openmpi

Both cmake and openmpi are designed to help with the computational efficiency of our program. We'll cover their usage in a more in-depth manner as we work through the code.

MuJoCo physics engine

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