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)

Introduction

Traditional robotics, known as Robotics Process Automation, is the process of automating physical tasks that would normally be done by a human. Much like the term machine learning covers a variety of methods and approaches, including deep learning approaches; robotics covers a wide variety of techniques and methods. In general, we can break these approaches down into two categories: traditional approaches and AI approaches.

Traditional robotic control programming takes a few steps:

  1. Measurement: The robot receives data from its sensors regarding actions to take for a given task.
  2. Inference: The orientation of the robot is relative to its environment from the data received in the sensors.
  3. Modeling: Models what the robot must do at each state of action to complete an action.
  4. Control: Codes the low-level controls, such as the steering mechanism, that the model will...