50 ML Projects To Understand LLMs

Most books teach you how to build LLMs from scratch or deploy them via APIs. This book uses guided machine learning projects to teach you how to understand, visualize, and investigate LLMs including GPT and BERT.Key FeaturesEach project is built around three learning goals: machine learning techniques, LLM mechanisms, and Python coding with data visualization.This is not a dense theoretical textbook; it's hands-on, practical, and project-oriented.You will learn how to measure, visualize, and manipulate the internal components of LLMs directly.Book DescriptionThrough 50 hands-on, guided projects solved in Python, you will investigate the internal mechanisms of large language models by treating their hidden states, attention patterns, and embeddings as data to analyze. Rather than accepting LLMs as black boxes, you will open them up, examine what's inside, and run experiments to understand why they behave the way they do. All projects are based on Python (using libraries such as NumPy, PyTorch, statsmodels, scikit-learn, Matplotlib, Pandas, and Seaborn) and come with full solutions and partial solution notebook files, so you can practice and improve your skills in data science, deep learning, data visualization, and scientific and statistical coding.What you will learnTokenization schemes and their statistical propertiesEmbedding spaces: cosine similarity, semantic axes, and analogy vectorsOutput logits, softmax distributions, perplexity, and language biasesLayer-by-layer transformer dynamics and dimensionalityAttention mechanisms: QKV weights, attention scores, head ablation, and activation patchingMLP subblocks: neuron tuning, mutual information, subspace analysis, and statistics-based causal manipulationsLogit lens, indirect object identification, and causal tracingWho this book is forThis book is for data scientists, ML engineers, and researchers who want to go beyond surface-level understanding of LLMs. Prior Python experience is required. Familiarity with machine learning or deep learning is helpful but not required — techniques are introduced as they arise throughout the projects.