1A Comprehensive Introduction to Differential Geometry, Vol. 1
Managing Director/Thiel Capital
Eric Weinstein recommended this book on Twitter. [source]
We've ranked the best differential geometry books using expert recommendations, sales data, and millions of reader ratings. At Shortform, we know books. Our book guides are the best in the world. Learn why.
1Managing Director/Thiel Capital
Eric Weinstein recommended this book on Twitter. [source]
Managing Director/Thiel Capital
Eric Weinstein recommended this book on Twitter. [source]
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5Managing Director/Thiel Capital
Eric Weinstein recommended this book on Twitter. [source]
6Managing Director/Thiel Capital
Eric Weinstein recommended this book on Twitter. [source]
7Managing Director/Thiel Capital
Eric Weinstein recommended this book on Twitter. [source]
10Managing Director/Thiel Capital
Eric Weinstein recommended this book on Twitter. [source]
12This is a firecracker of a book—a shot across the bows of theoretical physics. Sabine Hossenfelder, a theoretical physicist working on quantum gravity and blogger, confronts failures in her field head-on. [source]
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19Managing Director/Thiel Capital
Eric Weinstein recommended this book on Twitter. [source]
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48A Step-by-Step Workbook for Learning the Geometry Behind Modern Machine LearningDifferential Geometry for Machine Learning is a guided workbook for practitioners and students who keep running into manifolds, embeddings, and constrained optimization in their work but have never had a clear, computational path to actually understanding the geometry behind them. Instead of presenting abstract theorems, this workbook walks through each concept using solved examples, guided problems, and complete solutions, so you build real intuition alongside the math.Many machine learning tasks quietly assume flat, Euclidean space when the underlying data is not flat at all — normalized embeddings, probability distributions, rotations, and covariance matrices all live on curved spaces. This workbook explains why that mismatch causes subtle errors, and how differential geometry gives you the tools to reason about it correctly.What's InsideManifolds, Charts, and Atlases — how to describe curved spaces with local coordinates and understand why a single coordinate system often isn't enough.Tangent Spaces and Local Linearization — finding valid local directions and understanding which updates preserve a constraint.Differential Maps, Jacobians, Pushforwards, and Pullbacks — how tangent information moves through encoders, decoders, and other maps.Metrics and Measuring Geometry — how to define length, angle, and distance in a way that actually matches the structure of your data.Gradients on Manifolds — computing constraint-aware gradients instead of naive Euclidean ones.Geodesics, Exponential Maps, and Retractions — the correct way to interpolate and take optimization steps on curved spaces.Connections, Parallel Transport, and Curvature — comparing vectors at different points and understanding how curvature shapes learning spaces.Applications to Riemannian Optimization, Information Geometry, Lie Groups, Hyperbolic Embeddings, and Geometric Deep Learning — connecting the math directly to real modeling choices.How the Workbook Is StructuredEach chapter follows the same pattern: a clear explanation of the concept, worked examples that show the reasoning step by step, a list of common mistakes, and a set of practice problems with full solutions. Chapters build on each other, moving from the motivation for geometric thinking in Chapter 1 through coordinates, tangent spaces, metrics, gradients, and geodesics, before combining everything into a complete geometry-aware machine learning workflow in the final chapter.Appendices provide a symbol glossary, formula reference cards, a catalog of common machine learning manifolds, and cumulative review problems, so the workbook can also serve as an ongoing reference once you've worked through the main chapters.Who This Workbook Is ForMachine learning engineers working with embeddings, normalized representations, or constrained parametersGraduate students and researchers studying geometric deep learning or Riemannian optimizationPractitioners who have encountered terms like tangent space, geodesic, or pullback metric in papers and want to understand them properlyAnyone comfortable with linear algebra and multivariable calculus who wants a structured, problem-based path into differential geometryThis is a workbook meant to be worked through, not just read. Each solved problem is designed to be reworked by hand before checking the solution, and each chapter checklist helps confirm the ideas have actually settled in before moving forward.Scroll up and get your copy to start building a geometric foundation for your machine learning work.
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59Managing Director/Thiel Capital
Eric Weinstein recommended this book on Twitter. [source]
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72Written primarily for students who have completed the standard first courses in calculus and linear algebra, ELEMENTARY DIFFERENTIAL GEOMETRY, REVISED SECOND EDITION, provides an introduction to the geometry of curves and surfaces.
The Second Edition maintained the accessibility of the first, while providing an introduction to the use of computers and expanding discussion on certain topics. Further emphasis was placed on topological properties, properties of geodesics, singularities of vector fields, and the theorems of Bonnet and Hadamard.
This revision of the Second Edition provides a thorough update of commands for the symbolic computation programs Mathematica or Maple, as well as additional computer exercises. As with the Second Edition, this material supplements the content but no computer skill is necessary to take full advantage of this comprehensive text.
• Fortieth anniversary of publication! Over 36,000 copies sold worldwide
• Accessible, practical yet rigorous approach to a complex topic—also suitable for self-study
• Extensive update of appendices on Mathematica and Maple software packages
• Thorough streamlining of second edition's numbering system
• Fuller information on solutions to odd-numbered problems
• Additional exercises and hints guide students in using the latest computer modeling tools
Excellent brief introduction presents fundamental theory of curves and surfaces and applies them to a number of examples. Topics include curves, theory of surfaces, fundamental equations, geometry on a surface, envelopes, conformal mapping, minimal surfaces, more. Well-illustrated, with abundant problems and solutions. Bibliography.
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85Managing Director/Thiel Capital
Folks frequently ask “What are the books that changed your life?” If I tell them, they are usually radically disappointed. I find that curious. I just cleared out of an office, and these are 4 shelves of spines of books that mattered enough to me to bring home. So here they are. [source]
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