Book Image

Artificial Intelligence with Python - Second Edition

By : Prateek Joshi
Book Image

Artificial Intelligence with Python - Second Edition

By: Prateek Joshi

Overview of this book

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
Table of Contents (26 chapters)
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Building blocks of reinforcement learning

Apart from the interaction between the agent and the environment, there are other factors at play within the RL system:

Figure 1: Components of reinforcement learning

Typically, RL agents perform the following steps:

  1. There is a set of states related to the agent and the environment. At a given point of time, the agent observes an input state to sense the environment.
  2. There are policies that govern what action needs to be taken. These policies act as decision-making functions. The action is determined based on the input state using these policies.
  3. The agent takes the action based on the previous step.
  4. The environment reacts in response to that action. The agent receives reinforcement, also known as reward, from the environment.
  5. The agent calculates and records the information about this reward. It's important to note that this reward is received for this state/action pair so that it can be used to take...