course · HACKER NEWS RECOMMENDATION
Lecture 01 - The Learning Problem
caltech · 5 HN recommenders · 5 recommendation months
Recommended by 5 Hacker News commenters across 5 months (2016–2023).
SignalTube analysis
Among 4 other course videos tagged Machine learning & deep learning, the mean is 6.0 distinct HN recommenders. This video has 5 distinct HN recommenders, below that mean. Recommendations span 5 distinct months; the peer average is 5.8 months. This is a data-derived comparison, not a human review or quality rating.
83% of its 6 labeled comments are marked as recommendations or strong recommendations. HN mention years are 2016–2023; those years describe discussion history, not upload date. Its verified runtime is 81 minutes 28 seconds. YouTube lists its publication date as 2012-08-28.
- Channel / speaker
- caltech
- Verified runtime
- 1h 21m 28s
- YouTube upload date
- 2012-08-28
- Hacker News mention years
- 2016–2023
SAME TOPIC AND FORMAT
Compare with related videos
The comparison cohort uses other course videos in Machine learning & deep learning. “Shared recommenders” counts HN users who recommended both videos in the source graph.
| Video | HN recommenders | Recommendation months | Positive label share | Shared recommenders | Runtime | Upload date |
|---|---|---|---|---|---|---|
| Lecture 01 - The Learning ProblemCurrent video | 5 | 5 | 83% | — | 1h 21m 28s | 2012-08-28 |
| Lecture 1 | Machine Learning (Stanford)course · Stanford | 11 | 11 | 79% | 1 (20%) | 68m 40s | 2008-07-23 |
| RL Course by David Silver - Lecture 1: Introduction to Reinforcement Learningcourse · Google DeepMind | 9 | 9 | 91% | No recorded overlap | 88m 13s | 2015-05-13 |
| Practical Deep Learning for Coders: Lesson 1course · Jeremy Howard | 2 | 2 | 67% | No recorded overlap | 82m 56s | 2022-07-21 |
THREE SOURCE-BASED CONTEXT POINTS
Why Hacker News recommends it
Each note combines the dataset’s stance label and short rationale; comment bodies are not reproduced. Open the HN source for the original discussion.
The archive classifies this as a recommendation in “Ask HN: In 2023 which is the best path to get good at machine and deep learning?”. Its short rationale: Listed as a learning resource for ML.
The archive classifies this as a strong recommendation in “Ask HN: I am a full stack developer, where do I start learning for AI”. Its short rationale: Most elucidating ML intro resource, delivered clearly.
The archive classifies this as a recommendation in “Ask HN: How to Seriously Start with Machine Learning and AI”. Its short rationale: Online lectures explaining the math and context behind each topic.