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Scholarly Technical Roadmaps

Free material for hard books, in order.

Lectures, sample chapters, and problem sets published free by the authors and universities who created them. Sequenced section by section.

Open the roadmapHow it works

No paywalls · No accounts · Progress kept on your device

Interactive PreviewPart I: Highlights of Linear Algebra
I.1
Multiplication Ax Using Columns of ARead the section, Lecture 1, Problem set
I.2
Matrix-Matrix Multiplication ABRead the section, Lecture 2, Problem set
I.3
The Four Fundamental SubspacesProblem set, Locate in the contents
I.4
Elimination and A = LUProblem set, Locate in the contents
ReadNext up (where you left off)
The Challenge

The material is already free. It is almost never in the book's order.

A serious textbook often has a great deal published around it. The author posts sample chapters. The university puts the entire course online as video. The problem sets and the solutions go up too.

Then it sits there, unusable. Lectures are numbered by the course, not the book. Three sections have a free chapter and the other forty-three do not. The problem sets arrive as one combined file. Working out what to read after section I.7 means searching three sites, every time.

These roadmaps put that material back into the book's own sequence and keep your place in it automatically.

Roadmaps Catalog & Discovery

Explore Technical Roadmaps

Structured step-by-step paths through hard textbooks, with free lectures, problem sets, and personal progress tracking.

Topic:
Level:
Showing 2 of 2 roadmaps
A=Linear Algebra and Learning from DataGilbert Strang
First Editionadvanced

Linear Algebra and Learning from Data

Gilbert Strang · 2019

Gilbert Strang's graduate text connects the linear algebra you half-remember to the mathematics running underneath modern machine learning: the SVD, low-rank approximation, optimization, and the structure of neural networks. This roadmap walks all 46 sections in order and points each one at the material MIT publishes for free.

Linear AlgebraDeep LearningOptimizationData Science
Reading Progress0%
7 Parts · 46 Sections
Roadmap
Introduction to Linear AlgebraGilbert Strang
Sixth Editionintroductory

Introduction to Linear Algebra

Gilbert Strang · 2023

Gilbert Strang's world-renowned introductory textbook connects the geometry of vectors and elimination to the fundamental subspaces, eigenvalues, the SVD, and modern optimization. This roadmap covers all 10 chapters and maps each section to Professor Strang's famous MIT 18.06 video lectures and problem sets.

Linear AlgebraApplied MathematicsData Science
Reading Progress0%
10 Parts · 39 Sections
Roadmap
Features & Structure

What a roadmap carries

01

The contents, in sequence

Every section of the book in the order the author wrote it, each one linked to the lecture that covers it, the problem set, and the chapter itself where a free one exists.

02

Your place, and how far in

Tick sections off as you read. The next one is marked, so returning after two weeks away takes one glance rather than five minutes of hunting.

03

Study guides, where they help

For key sections we write our own: what the section is really about, what it sets up later in the book, and where people reliably get stuck.

Open Access Commitment

What we host, and what we do not.

We host no copyrighted books, chapters or PDFs. None are in the repository and none are served from this domain. Every reading link points at the server of the author or the university that chose to publish that material freely.

There is no account, no advertising, no tracking, and nothing for sale. Your progress is kept in your own browser and is never sent anywhere, because there is nowhere to send it. The site is static files on a free tier, which is why it can stay this way.

Where a book is sold, we link to buy it. Following a roadmap is a supplement to owning the book, not a replacement for it. The full position is on the copyright page.