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)
24
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25
Index

Reinforcement learning versus supervised learning

A lot of current research is focused on supervised learning. RL might seem a bit like supervised learning, but it is not. The process of supervised learning refers to learning from labeled samples. While this is a useful technique, it is not enough to start learning from interactions. When we want to design a machine to navigate unknown terrains, this kind of learning is not going to help us. We don't have training samples available beforehand.

We need an agent that can learn from its own experience by interacting with the unknown terrain. This is where RL really shines.

Let's consider the exploration stage when the agent is interacting with the new environment in order to learn. How much can it explore? At this point, the agent doesn't know how big the environment is, and in many cases, it won't be able to explore all the possibilities. So, what should the agent do? Should it learn from its limited experience...