計測済みの棚AI・機械学習
AI and machine learning videos, ranked by how much of them is useful
- 水増しの中央値
- 37%
- 有用な部分の開始
- 1:08
- 典型的な尺
- 24 min
最終再計算 2026/9/16
This is the category where the gap between reputation and substance is widest, because the subject rewards two very different videos that look identical from the outside.
The first kind spends its runtime on analogy. Neurons are like brain cells, attention is like paying attention, the model is like a very good autocomplete. It is pleasant, it is often accurate, and after forty minutes you cannot do anything you could not do before. The second kind uses one analogy to get you onto the ramp and then shows the actual arithmetic. Both get the same thumbnail treatment, and both are described as “explained”.
The measurement that separates them is density: what share of the runtime introduces something the previous minute did not already contain. Repetition of an idea in three different metaphors scores low here, and that is deliberate — it is the single most common way a fifteen-minute idea becomes a fifty-minute video in this subject.
Entries also carry a difficulty level, because “explained simply” and “explained” are not the same shelf, and the fastest way to waste an hour is to pick the wrong one for where you already are.
-
01
Let's reproduce GPT-2 (124M)
0:004:01:26- 密度
- 84
- 水増し
- 27%
- 有用な部分は
- 3:34
A code-complete, first-principles GPT-2 reproduction — one of the most valuable hands-on deep learning tutorials available.
-
02
But what is a convolution?
0:0023:01- 密度
- 84
- 水増し
- 24%
- 有用な部分は
- 1:41
A masterclass build-up of convolution — from dice probabilities to a genuinely surprising O(n log n) FFT algorithm.
-
03
Attention in transformers, step-by-step | Deep Learning Chapter 6
0:0026:10- 密度
- 83
- 水増し
- 21%
- 有用な部分は
- 1:40
A masterfully clear, dense walkthrough of the attention mechanism that rewards careful watching with real technical understanding.
-
04
But what is a neural network? | Deep learning chapter 1
0:0018:40- 密度
- 81
- 水増し
- 26%
- 有用な部分は
- 2:39
A rare, genuinely from-scratch, rigorous and re-derivable explanation of what a neural network's math actually is.
-
05
Transformers, the tech behind LLMs | Deep Learning Chapter 5
0:0027:14- 密度
- 80
- 水増し
- 26%
- 有用な部分は
- 1:26
A masterclass primer on transformer internals — dense, rigorous, and exactly what its title promises.
-
06
Deep Dive into LLMs like ChatGPT
0:003:31:24- 密度
- 82
- 水増し
- 27%
- 有用な部分は
- 1:07
One of the clearest, most complete plain-English explanations available of how ChatGPT-style LLMs are actually built and why they behave the way they do.
-
07
Let's build GPT: from scratch, in code, spelled out.
0:001:56:20- 密度
- 82
- 水増し
- 35%
- 有用な部分は
- 14:11
A masterclass build-along: real working GPT code, the actual mechanics behind ChatGPT, with almost no filler.
-
08
But how do AI images and videos actually work? | Guest video by Welch Labs
0:0037:20- 密度
- 82
- 水増し
- 22%
- 有用な部分は
- 3:28
A dense, physics-grounded explainer that actually teaches how diffusion models and CLIP combine to generate AI images and video — rare, high-value technical content.
-
09
But what is quantum computing? (Grover's Algorithm)
0:0036:54- 密度
- 82
- 水増し
- 24%
- 有用な部分は
- 0:52
A rigorous, honest deep dive that builds Grover's algorithm from first principles — 3Blue1Brown at its best.
-
10
What is backpropagation really doing? | Deep learning chapter 3
0:0012:47- 密度
- 80
- 水増し
- 33%
- 有用な部分は
- 0:53
A masterclass in building genuine intuition for backpropagation without a single formula — top-tier education.
-
11
[1hr Talk] Intro to Large Language Models
0:0059:48- 密度
- 80
- 水増し
- 25%
- 有用な部分は
- 3:31
A dense, ad-free, masterclass-level explainer of how LLMs are built, used, and attacked — among the best general AI education videos available.
-
12
Backpropagation calculus | Deep Learning Chapter 4
0:0010:18- 密度
- 80
- 水増し
- 25%
- 有用な部分は
- 0:32
A tight, rigorous derivation of backprop's chain-rule math — dense, durable, exactly what the title promises.
-
13
Gradient descent, how neural networks learn | Deep Learning Chapter 2
0:0020:33- 密度
- 79
- 水増し
- 28%
- 有用な部分は
- 1:52
A masterclass in building genuine intuition for gradient descent, honest about the method's real limitations.
-
14
Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!
0:0036:15- 密度
- 79
- 水増し
- 23%
- 有用な部分は
- 1:20
A rigorous, worked-numbers walkthrough of transformer internals that actually teaches how ChatGPT-style models work, not just what they do.
-
15
Let's build the GPT Tokenizer
0:002:13:35- 密度
- 82
- 水増し
- 31%
- 有用な部分は
- 4:22
An exceptionally dense, hands-on build of a real GPT tokenizer that also explains — with concrete transcript-verifiable examples — why tokenization is the hidden cause of most weird LLM…
-
16
How might LLMs store facts | Deep Learning Chapter 7
0:0022:43- 密度
- 82
- 水増し
- 22%
- 有用な部分は
- 0:58
A rigorous, self-contained walkthrough of how transformer MLP blocks might encode facts, capped with a genuinely illuminating dive into superposition and near-orthogonality in high…
-
17
The Most Important Algorithm in Machine Learning
0:0040:08- 密度
- 78
- 水増し
- 27%
- 有用な部分は
- 1:28
A rigorous, ground-up derivation of backpropagation that earns its title without needing hype.
-
18
Why Does Diffusion Work Better than Auto-Regression?
0:0020:18- 密度
- 79
- 水増し
- 32%
- 有用な部分は
- 0:32
A rigorous, original explanation of why diffusion models generate images faster than autoregressive ones — genuinely worth the watch.
-
19
The Essential Main Ideas of Neural Networks
0:0018:54- 密度
- 76
- 水増し
- 29%
- 有用な部分は
- 1:54
A rare, fully worked walkthrough of the actual math inside a neural network — StatQuest at its clearest.
-
20
Deep RL Bootcamp Lecture 4B Policy Gradients Revisited
0:0034:55- 密度
- 76
- 水増し
- 28%
- 有用な部分は
- 1:08
A superb, intuitive deep dive into policy gradients with a real code walkthrough — genuinely teaches, nothing being sold.
-
21
Flow-Matching vs Diffusion Models explained side by side
0:0016:08- 密度
- 75
- 水増し
- 32%
- 有用な部分は
- 0:33
A dense, well-structured technical breakdown that delivers exactly what its title promises: diffusion vs flow matching, math and all.
-
22
Diffusion Models | DDPM Explained
0:0029:29- 密度
- 81
- 水増し
- 20%
- 有用な部分は
- 1:18
A rigorous, self-derived walkthrough of DDPM math that earns its 'explained' title with real depth, not just paper restatement.
-
23
Diffusion Models: DDPM | Generative AI Animated
0:0032:06- 密度
- 79
- 水増し
- 22%
- 有用な部分は
- 1:37
A dense but genuinely rigorous derivation of DDPM from theory to working code — one of the clearer deep explainers on the topic.
-
24
Diffusion Models From Scratch | Score-Based Generative Models Explained | Math Explained
0:0038:11- 密度
- 79
- 水増し
- 27%
- 有用な部分は
- 1:20
A rigorous, derivation-heavy walkthrough that genuinely explains where the diffusion model equations come from, not just what they are.
-
25
The AI Progress Chart Everyone Is Misreading — Beth Barnes & David Rein
0:001:53:27- 密度
- 78
- 水増し
- 28%
- 有用な部分は
- 3:58
A rare, unusually candid deep-dive by the researchers themselves into how the AI 'progress chart' is built, where it breaks, and why the public keeps over-reading it.
Gistil's own measurement. Not a YouTube rating, and not the channel's position. 順位は私たちのものですが、動画自体はそれぞれのチャンネルに属します。
この順位はどう決まるのか
ここにあるすべての動画は同じ軸で計測されています——尺のどれだけが情報を運んでいるか、どれだけが水増しか、そして何秒から元が取れ始めるか。並び順は価値密度によるもので、再生回数や新しさ、私たちのチャンネルへの好みによるものではありません。数字が意味するもの →