1 Introducing deep learning and the PyTorch Library
This chapter covers
- How deep learning changes our approach to machine learning
- Understanding why PyTorch is a good fit for deep learning
- Examining a typical deep learning project
- The hardware you’ll need to follow along with the examples
The poorly defined term artificial intelligence covers a set of disciplines that have been subjected to a tremendous amount of research, scrutiny, confusion, fantastical hype, and sci-fi fearmongering. Reality is, of course, far more sanguine. It would be disingenuous to assert that today’s machines are learning to “think” in any human sense of the word. Rather, we’ve discovered a general class of algorithms that are able to approximate complicated, nonlinear processes very, very effectively, which we can use to automate tasks that were previously limited to humans.
For example, ChatGPT (https://chat.openai.com/), a chatbot developed by AI research company OpenAI, utilizes a large language model which can answer queries and generate paragraphs of text. When we fed it the prompt "Give an introduction about yourself through a poem," it produced the following:[1]
In realms of words, I come alive,
A creation born to help and thrive.
I’m ChatGPT, an AI so grand,
With knowledge vast, at your command.Through the art of language, I’ve been trained,
To assist and guide, with wisdom ingrained.
From lofty thoughts to mundane chit-chat,
I’m here to converse, imagine that!