Video course · 12 chapters · 75 min · certificate
Stanford CS230: AI Project Strategy - Speed, Data, Error Analysis
Stanford CS230 on running AI projects well: a voice-device example, why speed matters, literature search, data collection and training-set construction, unbalanced data, overfitting and data mismatch, the ML debugging cycle, a research pipeline and error analysis.
What you'll learn
- Plan an AI project with speed in mind
- Search literature efficiently
- Collect data and build training sets
- Debug unbalanced data, overfitting and mismatch
- Run a fast ML debugging cycle
- Use error analysis on multi-step pipelines
Chapters
12 chapters · 75:18-
2:50
01Intro Members
AI project strategy overview
Continuing the discussion on AI project strategy.
-
7:57
02Example Members
The voice device example
Building a voice-activated device.
-
3:30
03Speed Members
Speed and research
Many good ideas exist; speed of iteration wins.
-
4:23
04Research Members
Literature search
Tips for searching the literature.
-
6:36
05Data Members
Data collection
Collecting data to distinguish the wake phrase.
-
4:50
06Data Members
Constructing the training set
Building training data, including synthesis.
-
7:21
07Debugging Members
Debugging unbalanced data
A system that always predicts 'no'.
-
7:16
08Debugging Members
Overfitting and data mismatch
Doing well on train but not dev; mismatched distributions.
-
3:45
09Speed Members
Iteration and speed in ML
What ML work feels like: try, measure, adjust.
-
7:15
10Debugging Members
The ML debugging cycle
Deciding between regularization, more data or fixing distributions.
-
6:26
11Pipelines Members
An AI research pipeline
Building a basic deep-researcher pipeline and why speed matters.
-
13:09
12Error analysis Members
Error analysis in pipelines
Finding which component causes failures.
Jobs this course suits
Our AI checked this course against 500 jobs; these get the most out of it. Each job links to its learning path.