Apply modern AI techniques across science, engineering, and healthcare domains Modern Machine Learning and Transformers began on long walks on New York’s Vischer Ferry trail and became an accessible yet rigorous gateway to Modern AI. Two authors—one focused on multivariate statistics and interpretability, the other in industrial, neural-network-driven modeling—converge on a practical philosophy: build models you can trust. Across eleven mostly stand-alone chapters, intuition comes first, implementation follows, and deeper math lives in later sections and appendices. Regression and logistic regression appear as a Gauss-Legendre network, introducing weights, learning rates, and stochastic gradient descent early. A historically informed arc links classical regression and classification to backpropagation and today’s transformers. Case studies span science, engineering, healthcare analytics, and scientific computing, showing what works, what fails, and why. Readers will also find: Neural-network viewpoint early: regression and classification become trainable networks via gradient descent and principled learning rates.Concept-first pedagogy: intuitive explanations, geometric insights, then rigorous derivations in later sections and appendices when needed.Self-contained chapters allow building flexible learning outcomes; companion slide sets support undergraduate and graduate classrooms.Case studies from research and industrial R&D cover autism, drug design, and other science, engineering, and healthcare applications—trade-offs explained.Transformers demystified: attention, scaling, BERT-like designs, and practical guidance for building chatbots without blind hype.Whether you are preparing for Modern AI or refreshing your skills, you will learn to choose methods wisely, validate honestly, and recognize failure modes. You leave with code-ready intuition for classical models, deep networks, and transformers—plus perspectives on advanced GPU workflows and emerging quantum-enabled learning.

Apply modern AI techniques across science, engineering, and healthcare domains Modern Machine Learning and Transformers began on long walks on New York’s Vischer Ferry trail and became an accessible yet rigorous gateway to Modern AI. Two authors—one focused on multivariate statistics and interpretability, the other in industrial, neural-network-driven modeling—converge on a practical philosophy: build models you can trust. Across eleven mostly stand-alone chapters, intuition comes first, implementation follows, and deeper math lives in later sections and appendices. Regression and logistic regression appear as a Gauss-Legendre network, introducing weights, learning rates, and stochastic gradient descent early. A historically informed arc links classical regression and classification to backpropagation and today’s transformers. Case studies span science, engineering, healthcare analytics, and scientific computing, showing what works, what fails, and why. Readers will also find: Neural-network viewpoint early: regression and classification become trainable networks via gradient descent and principled learning rates.Concept-first pedagogy: intuitive explanations, geometric insights, then rigorous derivations in later sections and appendices when needed.Self-contained chapters allow building flexible learning outcomes; companion slide sets support undergraduate and graduate classrooms.Case studies from research and industrial R&D cover autism, drug design, and other science, engineering, and healthcare applications—trade-offs explained.Transformers demystified: attention, scaling, BERT-like designs, and practical guidance for building chatbots without blind hype.Whether you are preparing for Modern AI or refreshing your skills, you will learn to choose methods wisely, validate honestly, and recognize failure modes. You leave with code-ready intuition for classical models, deep networks, and transformers—plus perspectives on advanced GPU workflows and emerging quantum-enabled learning.