Exploring My ( Darrell Ulm ) Tech‑Focused GoodReads Profile: What I’m Reading in Machine Learning, Big Data, and Modern Software Engineering
My GoodReads profile. Darrell Ulm : Good Reads, has become a kind of living map of my journey through computer science, machine learning, and large‑scale software systems. Most of the books I read — and the ones I’m planning to read next revolve around programming, Big Data, AI, Drupal, Apache Spark, and the deeper mechanics of how modern computing actually works.
If you’re interested in machine learning books, LLM development, or high‑performance computing, my reading list might feel like a curated guide through today’s most important technologies.
📘 Why Build a Large Language Model (From Scratch) Stands Out
One of the highlights of my recent reading is Sebastian Raschka’s Build a Large Language Model (From Scratch), which I rated 5 stars. I described it this way:
“A highly valuable resource for anyone seeking a deep, hands‑on understanding of LLMs… guiding readers through the process of constructing an LLM from the ground up, rather than simply fine‑tuning existing models.”
This book hit exactly what I look for: practical, foundational, and deeply technical. Instead of treating LLMs as mysterious black boxes, it walks through the architecture and engineering decisions that make them work. For anyone serious about AI engineering, this one is essential.
📚 My Machine Learning Reading Queue
My “Want to Read” list is packed with more of Raschka’s work — a testament to how consistently useful his books have been for me. These are next on my list:
Python Machine Learning & Deep Learning (scikit‑learn + TensorFlow 2)
Machine Learning with PyTorch and Scikit‑Learn
Machine Learning Q and AI: 30 Essential Questions and Answers
Machine Learning con PyTorch y Scikit‑Learn (Spanish Edition)
I’m intentionally building a strong cross‑framework understanding of ML , TensorFlow, PyTorch, scikit‑learn, because each ecosystem brings its own strengths. I also enjoy reading technical material in multiple languages when possible.
🧠 My Foundation in Parallel and High‑Performance Computing
Long before LLMs became mainstream, I was fascinated by parallel computation and distributed systems. That’s why my bookshelf includes classics like:
Parallel Computation : Selim G. Akl
Highly Parallel Computations : M.P. Bekakos
Natural and Artificial Parallel Computation : Michael A. Arbib
Software for Parallel Computation : Janusz S. Kowalik
These books shaped how I think about scalability, concurrency, and the computational limits of hardware — all of which tie directly into Big Data systems like Apache Spark, Hadoop, and modern cloud‑native architectures.
🔍 Why My GoodReads Profile Might Help Other Tech Learners
If you’re exploring:
machine learning books for beginners or advanced practitioners
resources for learning PyTorch, TensorFlow, or scikit‑learn
how to build large language models
foundational texts on parallel computing
A GoodReads profile offers a curated, experience‑driven reading path through all of it, and it's a good site to find books you would be interested.
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