MachineLearning-QandAI-book

Why we included this project

Sebastian Raschka's Machine Learning Q and AI book comes with a repo of notebooks that let you try out the ideas rather than just read them. A chapter on self-attention includes code to reimplement the mechanism, and elsewhere you can test how dropout or random seeds change results, compare metrics like BLEU, ROUGE, and BERTScore, or see how conformal prediction stacks up against confidence intervals. The topics span vision transformers and fine-tuning as well as production concerns such as speeding up inference and handling data distribution shifts, so it suits engineers who know the basics and want to close gaps. Because the author is an experienced educator, the code is organized for learning rather than production packaging, which makes it straightforward to pull apart and reuse.