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 term artificial intelligence encompasses a range of disciplines that have recently gained significant attention, often leading to varied interpretations and definitions. While some discussions have been marked by hype and fearmongering reminiscent of science fiction, the reality is more measured. 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. While this may seem straightforward or even underwhelming, it has already greatly expanded our understanding of what is possible with computers.
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]