diff --git a/instructors/404.html b/instructors/404.html index c7b9325360..57ac6d0b96 100644 --- a/instructors/404.html +++ b/instructors/404.html @@ -738,7 +738,7 @@ ul.task-list li input[type="checkbox"] {
  • - + Subscribe
    • @@ -1894,7 +1894,9 @@ ul.task-list li input[type="checkbox"] { ' · hash ' + shortHash; pills.forEach(function (el) { - var linkHref = el.getAttribute('data-link') || '#release'; + var rawLink = el.getAttribute('data-link') || '#release'; + // XSS hardening: allow only anchor, relative, or http(s) links; reject javascript:/data: URLs. + var linkHref = /^(?:#|\/|\.{1,2}\/|https?:\/\/)/i.test(rawLink) ? rawLink : '#release'; var a = document.createElement('a'); a.href = linkHref; a.title = title; diff --git a/instructors/faq.html b/instructors/faq.html index 14b56eaf98..78055453fe 100644 --- a/instructors/faq.html +++ b/instructors/faq.html @@ -739,7 +739,7 @@ ul.task-list li input[type="checkbox"] {
  • - + Subscribe
    • @@ -1170,7 +1170,7 @@ window.Quarto = { cd cs249r_book/tinytorch
    • Students work in src/ — one folder per module (e.g., src/01_tensor/)
      • -
    • Each module has a companion ABOUT page with learning objectives, audio overview, and a Binder link for browser-based execution
      • +
    • Each module has a companion module page with learning objectives, implementation guidance, and tests
    • Auto-grading runs via pytest or nbgrader
    • See the TinyTorch Instructor Guide for nbgrader configuration
    @@ -1184,7 +1184,7 @@ window.Quarto = {
    -

    Each module is a Python file with scaffolded cells. Students implement core functions (forward(), backward(), etc.) while the test suite validates correctness. For example, in Module 06: Autograd, students build reverse-mode automatic differentiation from scratch — implementing the computation graph, topological sort, and gradient accumulation that powers every modern framework. Browse any module’s ABOUT page on the TinyTorch site to see the full structure.

    +

    Each module is a Python file with scaffolded cells. Students implement core functions (forward(), backward(), etc.) while the test suite validates correctness. For example, in Module 06: Autograd, students build reverse-mode automatic differentiation from scratch — implementing the computation graph, topological sort, and gradient accumulation that powers every modern framework. Browse any module page on the TinyTorch site to see the full structure.

    @@ -1266,8 +1266,8 @@ window.Quarto = {

    Here is exactly what to assign on Day 1:

    1. Reading: Vol I, Introduction (45 min)
      2. -
    2. Lab: Lab 00 — The Architect’s Portal (30 min)
      3. -
    3. TinyTorch: Module 01: Tensor — due end of Week 1 (4–6 hrs)
      4. +
    4. Lab: Lab 00 — The Architect’s Portal (30 min)
      5. +
    5. TinyTorch: Module 01: Tensor — due end of Week 1 (4–6 hrs)
    6. Prediction Lock: “A GPU is how many times faster than a CPU for a \(1024{\times}1024\) matrix multiply?” (students commit a number before Lab 01)
    @@ -1869,7 +1869,9 @@ window.Quarto = { ' · hash ' + shortHash; pills.forEach(function (el) { - var linkHref = el.getAttribute('data-link') || '#release'; + var rawLink = el.getAttribute('data-link') || '#release'; + // XSS hardening: allow only anchor, relative, or http(s) links; reject javascript:/data: URLs. + var linkHref = /^(?:#|\/|\.{1,2}\/|https?:\/\/)/i.test(rawLink) ? rawLink : '#release'; var a = document.createElement('a'); a.href = linkHref; a.title = title; diff --git a/instructors/index.html b/instructors/index.html index 13eafd4e43..bb92c45fe5 100644 --- a/instructors/index.html +++ b/instructors/index.html @@ -739,7 +739,7 @@ ul.task-list li input[type="checkbox"] {
  • - + Subscribe
    • @@ -1629,9 +1629,31 @@ ul.task-list li input[type="checkbox"] {
    Hardware Kits

    Hands-on embedded ML deployment on real devices.

    Instructor Hub

    Course maps, syllabi, and adoption resources for teaching.

    TinyTorch

    Build your own ML framework from scratch, module by module.

    -
    MLSysIM

    The physics engine behind every quantitative figure in the book.

    +
    MLSys·im

    The analytical modeling engine behind the book's quantitative figures.