Datasets:
video_id stringlengths 11 11 | title stringlengths 2 101 | text large_stringlengths 1 778k | duration_seconds float64 5.31 54.5k | source stringlengths 0 75 | url stringlengths 0 43 | priority int64 5 9 | speed_ratio float64 6.1 611 |
|---|---|---|---|---|---|---|---|
vidCX_dMCu0 | Lec 02. How to Train a Neural Net | Okay, so welcome to lecture two of deep learning. Today we're going to talk about how you actually train a neural network. And I imagine that some of this might be a review for some of you but hopefully kind of the perspective or the way we look talk about the process of training the neural network might be a slightly ... | 4,773.952 | 9 | 67.6 | ||
8zzfcYIELdo | Lec 15. Generative Models: Representation Learning Meets Generative Modeling | We're going to continue on today and this week with generative models. So today we'll be on variational auto-encoders. And we're going to give a perspective that generative modeling and representation learning are very tightly coupled. If you can solve the generative modeling problem, it should help you solve the repre... | 4,839.317333 | 9 | 63.7 | ||
7hbf4klU3ks | Lec 20. Scaling Laws | Okay, so today we're going to talk about scaling loss. And this lecture is, you know, we're at the final few weeks of the semester. So this is, we're moving into territory, which is much more kind of ongoing science. Like I'm gonna talk about recent papers and results that are empirical in nature for which we don't kno... | 2,302.037333 | 9 | 69.9 | ||
IiHknRHA-Gk | Lec 10. Architectures: Memory | "So today we're going to be doing our last of this mini series on machine learning architectures or (...TRUNCATED) | 4,407.616 | 9 | 70.7 | ||
bxVkZ4M-hIE | Lec 04. Architectures: Grids | "So today we're going to start off the first of a series of lectures on machine learning architectur(...TRUNCATED) | 5,036.309333 | 9 | 65.9 | ||
zaMcHuJwe1w | Lec 16. Generative Models: Conditional Models | "OK, so welcome. Happy Halloween. Nice to see a few little costumes. OK, so today we're going to tal(...TRUNCATED) | 4,891.285333 | 9 | 63.1 | ||
-eC0-5mXHQg | Lec 13. Representation Learning: Theory | "Okay, thank you everyone for coming to the lecture. So this was the example on the, do people remem(...TRUNCATED) | 4,520.106667 | 9 | 73.7 | ||
QxOzQRtd440 | Lec 11. Representation Learning: Reconstruction-Based | "Good. So welcome to today's lecture. And today we're going to talk about representation learning. S(...TRUNCATED) | 4,863.616 | 9 | 65.7 | ||
RUdQMHV-7KM | Lec 19. Transfer Learning: Data | "We're going to finish up our short series on transfer learning today. And the other sort of logisti(...TRUNCATED) | 4,543.104 | 9 | 68.2 | ||
hJlrAHqGOS8 | Lec 14. Generative Models: Basics | "Okay, yeah, welcome everyone. So now we're going to get coming to the, um, another section of the c(...TRUNCATED) | 4,877.909333 | 9 | 67.3 |
End of preview. Expand in Data Studio
Massive YouTube Educational Transcriptions
Large-scale educational content transcribed from YouTube using distil-whisper/distil-large-v3.5.
Stats
- Videos: 59,355
- Characters: 1,539,022,925 (~384M tokens)
- Audio hours: 35,890
- Model: faster-whisper (CTranslate2) with distil-large-v3.5
- Hardware: 2x RTX 5090 + 2x RTX 4090 at 165-185x realtime
Fields
| Field | Description |
|---|---|
video_id |
YouTube video ID |
title |
Video title |
text |
Full transcript |
duration_seconds |
Original audio duration (seconds) |
source |
Discovery source (channel/course/playlist) |
url |
YouTube URL |
priority |
Educational priority (9=university, 8=lecture, 7=documentary, 5=general) |
speed_ratio |
Transcription speed (realtime multiplier) |
content_category |
Content type (university_lecture, conference, individual_educator, etc.) |
license_risk |
License risk level (green/yellow/orange/red) |
Content Categories
- green: CC-licensed or public domain (NPTEL, Khan Academy, MIT OCW, Yale OYC)
- yellow: Standard YouTube license, fair use for research
- orange: Commercial content, needs review
- red: Non-educational, excluded from transcription
Methodology
- Discovery: Channel crawling, related video walking, CC-focused search
- Quality filter: 40+ reject categories, duration >= 15min, 3-tier priority scoring
- Transcription: faster-whisper CTranslate2, 1.2x audio speedup, beam=1, no VAD
- Classification: Channel name -> title regex -> priority fallback
Code
github.com/thepowerfuldeez/massive_yt_edu_scraper
License
MIT
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