course · HACKER NEWS RECOMMENDATION
Practical Deep Learning for Coders: Lesson 1
Jeremy Howard · 2 HN recommenders · 2 recommendation months
Recommended by 2 Hacker News commenters across 2 months (2022–2023).
SignalTube analysis
Among 4 other course videos tagged Machine learning & deep learning, the mean is 6.8 distinct HN recommenders. This video has 2 distinct HN recommenders, below that mean. Recommendations span 2 distinct months; the peer average is 6.5 months. This is a data-derived comparison, not a human review or quality rating.
67% of its 3 labeled comments are marked as recommendations or strong recommendations. HN mention years are 2022–2023; those years describe discussion history, not upload date. Its verified runtime is 82 minutes 56 seconds. YouTube lists its publication date as 2022-07-21.
- Channel / speaker
- Jeremy Howard
- Verified runtime
- 1h 22m 56s
- YouTube upload date
- 2022-07-21
- Hacker News mention years
- 2022–2023
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 |
|---|---|---|---|---|---|---|
| Practical Deep Learning for Coders: Lesson 1Current video | 2 | 2 | 67% | — | 1h 22m 56s | 2022-07-21 |
| Lecture 1 | Machine Learning (Stanford)course · Stanford | 11 | 11 | 79% | No recorded overlap | 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 |
| Lecture 01 - The Learning Problemcourse · caltech | 5 | 5 | 83% | No recorded overlap | 81m 28s | 2012-08-28 |
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 contextual reference in “I made a transformer by hand (no training)”. Its short rationale: Shows pre-deep-learning hand-tuned image recognition.
The archive classifies this as a strong recommendation in “Ask HN: What's the best lecture series you've ever seen?”. Its short rationale: Hands-on lectures run in Jupyter, with Kaggle deep dives.
The archive classifies this as a recommendation in “Riffusion – Stable Diffusion fine-tuned to generate Music”. Its short rationale: First lesson covers image techniques applied to audio spectrograms.