YOUR FOUNDATION-FIRST SYLLABUS
Video generation
Build on pictures to follow a sequence of frames. Explore how a generator learns movement, follows a description, and keeps objects consistent as the scene changes.
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Chapter 1 · Lessons 1–7 Meet an AI system Start with an everyday task, then follow its inputs, model, and output. 7 lessons · 0 of 7 core ideas demonstrated Up next
01
What is AI? Start with an everyday task Common Ground · 11 min
To explore02 Inputs and outputs: what goes in and what comes out Common Ground · 13 min
To explore03 Data and examples: what a computer can learn from Common Ground · 10 min
To explore04 What is a model? A small rule inside a bigger app Common Ground · 10 min
To explore05 Weights: the adjustable numbers in a model Common Ground · 12 min
To explore06 Training and inference: changing a rule or using it Common Ground · 13 min
To explore07 Follow one small AI system from start to finish Common Ground · 9 min
To exploreChapter 2 · Lessons 8–13 How computers represent a problem: How a computer stores a picture or a sentence Explore everyday AI ideas; how AI got here; how computers represent a problem. 6 lessons · 0 of 6 core ideas demonstrated
08
Different ways to solve the same problem Common Ground · 11 min
To explore09 How to tell whether an AI answer is useful Common Ground · 10 min
To explore10 The questions that shaped AI Common Ground · 15 min
To explore11 How a computer stores a picture or a sentence Learning mechanisms · 16 min
To explore12 Describe a computation so someone else can follow it Learning mechanisms · 11 min
To explore13 What a representation makes easy to learn Learning mechanisms · 12 min
To exploreChapter 3 · Lessons 14–20 How computers represent a problem: Trace an example through a batch Explore how computers represent a problem; learning from sequences and sound; math preparation when it is needed. 7 lessons · 0 of 7 core ideas demonstrated
14
The numbers you actually need Math preparation · 10 min
To explore15 Vectors: lists that work together Math preparation · 10 min
To explore16 Read an equation one symbol at a time Math preparation · 10 min
To explore17 Follow shapes through a numerical layer Math preparation · 10 min
To explore18 Trace an example through a batch Learning mechanisms · 12 min
To explore19 A function is a rule Math preparation · 9 min
To explore20 From a waveform to time-frequency features Learning mechanisms · 14 min
To exploreChapter 4 · Lessons 21–27 Connecting words, images, and other signals Explore connecting words, images, and other signals; learning from useful data; learning to predict; and supporting ideas. 7 lessons · 0 of 8 core ideas demonstrated
21
Turn images, sound, and video into manageable model inputs Learning mechanisms · 14 min
To explore22 From the world to a dataset Learning mechanisms · 11 min
To explore23 Build a tiny prediction model Learning mechanisms · 11 min
To explore24 How does a model improve a prediction? Learning mechanisms · 32 min
To explore25 A derivative is a local change Math preparation · 13 min
To explore26 The chain rule, one step at a time Math preparation · 13 min
To explore27 How an error reaches earlier weights Learning mechanisms · 11 min
To exploreChapter 5 · Lessons 28–34 Generating something new Explore generating something new; making sense of images; math preparation when it is needed. 7 lessons · 0 of 7 core ideas demonstrated
28
How an image can emerge from noise Your system, connected · 21 min
To explore29 Probability without the mystery Math preparation · 10 min
To explore30 Predicting a label, generating a possibility Learning mechanisms · 14 min
To explore31 Why image generators work in latent space Your system, connected · 15 min
To explore32 Distances, neighborhoods, and transformations Math preparation · 12 min
To explore33 What changes when an image changes? Learning mechanisms · 13 min
To explore34 Averages over uncertain outcomes Math preparation · 9 min
To exploreChapter 6 · Lessons 35–41 Working with uncertainty Explore working with uncertainty; making sense of images; connecting words, images, and other signals; and supporting ideas. 7 lessons · 0 of 7 core ideas demonstrated
35
Probability, likelihood, and what is unknown Learning mechanisms · 11 min
To explore36 Update a belief using new evidence Learning mechanisms · 13 min
To explore37 Sets, graphs, and counting possibilities Math preparation · 10 min
To explore38 Factor a joint distribution into smaller pieces Learning mechanisms · 11 min
To explore39 Track a hidden state from noisy observations Learning mechanisms · 12 min
To explore40 Infer structure that a flat image does not directly reveal Learning mechanisms · 14 min
To explore41 Model motion and persistence across frames Learning mechanisms · 14 min
To exploreChapter 7 · Lessons 42–48 Inside a neural network Explore inside a neural network; helping a model improve; math preparation when it is needed. 7 lessons · 0 of 7 core ideas demonstrated
42
Inside an artificial neuron Learning mechanisms · 14 min
To explore43 Why layers need more than multiplication Learning mechanisms · 12 min
To explore44 Trace inputs through layers to a loss Learning mechanisms · 17 min
To explore45 Let the graph carry derivatives Learning mechanisms · 12 min
To explore46 Learn from a sample of examples at each step Learning mechanisms · 11 min
To explore47 Several inputs, several rates of change Math preparation · 9 min
To explore48 Choose a direction and a step size Math preparation · 9 min
To exploreChapter 8 · Lessons 49–55 Helping a model improve Explore helping a model improve; inside a neural network; math preparation when it is needed. 7 lessons · 0 of 7 core ideas demonstrated
49
How momentum and Adam use earlier gradients Learning mechanisms · 15 min
To explore50 Constrain a solution or change the update Learning mechanisms · 12 min
To explore51 Powers and logarithms, one step at a time Math preparation · 11 min
To explore52 When the computer cannot store the exact number Math preparation · 12 min
To explore53 Keep signals and gradients in a usable range Learning mechanisms · 12 min
To explore54 Help information and learning signals travel Learning mechanisms · 12 min
To explore55 Connect data, gradients, and saved model state Learning mechanisms · 15 min
To exploreChapter 9 · Lessons 56–63 Learning from useful data Explore learning from useful data; finding patterns and useful representations; inside a neural network; and supporting ideas. 8 lessons · 0 of 8 core ideas demonstrated
56
Different ways to learn Common Ground · 12 min
To explore57 Create a learning task from the data itself Learning mechanisms · 14 min
To explore58 What can a network’s hidden features tell us? Learning mechanisms · 12 min
To explore59 Connect information expressed in different forms Learning mechanisms · 15 min
To explore60 Turn a linear score into a class probability Learning mechanisms · 11 min
To explore61 What can a small experiment tell us? Math preparation · 10 min
To explore62 Clean data without erasing the problem Learning mechanisms · 12 min
To explore63 Protect the examples used to judge a model Learning mechanisms · 15 min
To exploreChapter 10 · Lessons 64–71 Learning to predict Explore learning to predict; testing what a model has learned; connecting words, images, and other signals; and supporting ideas. 8 lessons · 0 of 8 core ideas demonstrated
64
Control complexity without peeking at the answer Learning mechanisms · 12 min
To explore65 Evaluate the procedure that chose the model Learning mechanisms · 14 min
To explore66 Check whether answers actually follow the supplied media Learning mechanisms · 14 min
To explore67 Who is represented by the data? Learning mechanisms · 14 min
To explore68 Decide what success means before training Learning mechanisms · 12 min
To explore69 Look inside the average score Learning mechanisms · 14 min
To explore70 Value a choice and its later consequences Math preparation · 12 min
To explore71 Turn a probability into a justified action Learning mechanisms · 11 min
To exploreChapter 11 · Lessons 72–79 Checking reliability and behavior Explore testing what a model has learned; how computers represent a problem; checking reliability and behavior; and supporting ideas. 8 lessons · 0 of 8 core ideas demonstrated
72
Change one thing and measure what follows Learning mechanisms · 13 min
To explore73 Repeat an experiment without confusing luck with truth Learning mechanisms · 11 min
To explore74 Make the result possible to inspect Learning mechanisms · 12 min
To explore75 Measure the behavior that the task actually needs Learning mechanisms · 14 min
To explore76 Build a test whose score supports the intended claim Learning mechanisms · 12 min
To explore77 Treat evaluators as fallible measurement instruments Learning mechanisms · 17 min
To explore78 Decide whose problem the system solves and who bears its errors Learning mechanisms · 12 min
To explore79 Project: trace and evaluate a tiny video generator Your system, connected · 35 min
To exploreOptional extensions and comparisons (6)
These lessons deepen or compare the reference design. They do not add requirements to this path’s completion.
How to use this school without getting lostOptional orientation: how paths, side lessons, and practice fit together, plus study habits that make learning stick.Practice, progress, and knowing what you understandOptional orientation: use practice, mistakes, and spaced review to find out what you really understand.Misuse in practice: jailbreaks, deepfakes, and provenanceA familiar face or voice is no longer proof of who is speaking. Learn how misuse works and the checks that still hold.Predict what may happen next, with or without actionsModels that predict what happens next, for planning and generation.Learn a velocity field and integrate it into samplesThe fast successor to diffusion sampling: learn a velocity field and follow it from noise to image.Generate different kinds of data one unit at a timeGenerate images or video one token at a time, the way language models write, and compare that with diffusion.