To get the most out of this book

You should possess proficiency in Python coding and familiarity with Google Colab, alongside a foundational understanding of machine learning and deep learning principles.You also need to be familiar with machine learning frameworks like PyTorch.

This book is for individuals who possess a fundamental understanding of machine learning and deep learning and who aim to acquire knowledge about active learning in order to optimize the annotation process of their machine learning datasets. This optimization will enable them to train the most effective models possible.

Software covered in the book

Python packages: scikit-learn, matplotlib, numpy, datasets, transformers, huggingface_hub, torch, pandas, torchvision, roboflow, tqdm, glob, pyyaml, opencv-python, ultralytics, lightly, docker, encord, clearml, pymongo, and modAL-python

Jupyter or Google Colab notebook (with Python version 3.10.12 and above)

You will need to create accounts for diverse tools: Encord, Roboflow, and Lightly. You will also need access to an AWS EC2 instance for Chapter 6, Evaluating and Enhancing Efficiency.

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.