To get the most out of this book
You should have a basic understanding of Python and be familiar with fundamental programming concepts such as functions, classes, and modules. A general knowledge of machine learning and neural networks (such as what a model is and how training works) will help in following the deeper technical content. While prior experience with deep learning frameworks or LLMs is not required, it will enhance your ability to apply the techniques discussed. The book is designed to be progressive, so concepts are introduced step by step, but a technical mindset is essential.
|
Software/hardware covered in the book |
Operating system requirements |
|
Python 3.10+ |
Windows, macOS, or Linux |
|
PyTorch/Transformers |
Windows, macOS, or Linux |
|
Streamlit |
Windows, macOS, or Linux |
|
Docker |
Windows, macOS, or Linux |
For readers without access to a local GPU, using Google Colab is a convenient option. A Google Colab Pro account is recommended, as it provides access to more powerful GPUs such as NVIDIA T4 or A100, which can greatly improve performance when running embedding models, fine-tuning, or working with agents.
If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.
