github-starred/cs249r_book
2
0
Fork 0
mirror of https://github.com/harvard-edge/cs249r_book.git synced 2026-04-29 09:08:54 -05:00
Code Issues 77 Packages Projects Releases 24 Wiki Activity

Frame AI engineers as aspirational goal in abstract

Browse Source
This commit is contained in:
Vijay Janapa Reddi
2026-01-26 21:03:16 -05:00
parent 7c70577ab7
commit e4a664b9c1
1 changed files with 1 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
tinytorch/paper/paper.tex
View File

@@ -220,7 +220,7 @@
% Abstract - REVISED: Curriculum design focus
\begin{abstract}
Machine learning systems engineering requires understanding framework internals: why optimizers consume memory, when computational complexity becomes prohibitive, how to navigate accuracy-latency-memory tradeoffs. Yet current ML education separates algorithms from systems—students learn gradient descent without measuring memory, attention mechanisms without profiling costs, training without understanding optimizer overhead. This divide leaves graduates unable to debug production failures or make informed engineering decisions, widening the gap between ML research and reliable production deployment. We present TinyTorch, a build-from-scratch curriculum where students implement PyTorch's core components (tensors, autograd, optimizers, neural networks) to gain framework transparency. Three pedagogical patterns address the gap: \textbf{progressive disclosure} gradually reveals complexity (gradient features exist from Module 01, activate in Module 06); \textbf{systems-first curriculum} embeds memory profiling from the start; \textbf{historical milestone validation} recreates nearly 70 years of ML breakthroughs (1958--2025) using exclusively student-implemented code. These patterns are grounded in learning theory (situated cognition, cognitive load theory) but represent testable hypotheses requiring empirical validation. The 20-module curriculum prepares \emph{AI engineers}: practitioners who understand not just what ML systems do, but why they work and how to make them scale. Complete open-source infrastructure is available at \texttt{mlsysbook.ai/tinytorch}.
Machine learning systems engineering requires understanding framework internals: why optimizers consume memory, when computational complexity becomes prohibitive, how to navigate accuracy-latency-memory tradeoffs. Yet current ML education separates algorithms from systems—students learn gradient descent without measuring memory, attention mechanisms without profiling costs, training without understanding optimizer overhead. This divide leaves graduates unable to debug production failures or make informed engineering decisions, widening the gap between ML research and reliable production deployment. We present TinyTorch, a build-from-scratch curriculum where students implement PyTorch's core components (tensors, autograd, optimizers, neural networks) to gain framework transparency. Three pedagogical patterns address the gap: \textbf{progressive disclosure} gradually reveals complexity (gradient features exist from Module 01, activate in Module 06); \textbf{systems-first curriculum} embeds memory profiling from the start; \textbf{historical milestone validation} recreates nearly 70 years of ML breakthroughs (1958--2025) using exclusively student-implemented code. These patterns are grounded in learning theory (situated cognition, cognitive load theory) but represent testable hypotheses requiring empirical validation. The goal is to prepare the next generation of \emph{AI engineers}: practitioners who understand not just what ML systems do, but why they work and how to make them scale. Complete open-source infrastructure is available at \texttt{mlsysbook.ai/tinytorch}.
\end{abstract}