YOUR FOUNDATION-FIRST SYLLABUS
Computer vision
Follow how stored pixels become clues about what is in a picture and where it is. Learn how models use those clues and how to check when their answers fail.
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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 8–14 How computers represent a problem Explore everyday AI ideas; how AI got here; how computers represent a problem; and supporting ideas. 7 lessons · 0 of 7 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 explore14 The numbers you actually need Math preparation · 10 min
To exploreChapter 3 · Lessons 15–22 Inside a neural network: Inside an artificial neuron Explore making sense of images; learning from useful data; learning to predict; and supporting ideas. 8 lessons · 0 of 8 core ideas demonstrated
15
Vectors: lists that work together Math preparation · 10 min
To explore16 Distances, neighborhoods, and transformations Math preparation · 12 min
To explore17 What changes when an image changes? Learning mechanisms · 13 min
To explore18 From the world to a dataset Learning mechanisms · 11 min
To explore19 A function is a rule Math preparation · 9 min
To explore20 Build a tiny prediction model Learning mechanisms · 11 min
To explore21 Inside an artificial neuron Learning mechanisms · 14 min
To explore22 Why layers need more than multiplication Learning mechanisms · 12 min
To exploreChapter 4 · Lessons 23–29 Inside a neural network: Trace inputs through layers to a loss Explore inside a neural network; making sense of images; math preparation when it is needed. 7 lessons · 0 of 7 core ideas demonstrated
23
Read an equation one symbol at a time Math preparation · 10 min
To explore24 Follow shapes through a numerical layer Math preparation · 10 min
To explore25 Trace inputs through layers to a loss Learning mechanisms · 17 min
To explore26 Slide a shared filter across an input Learning mechanisms · 17 min
To explore27 A derivative is a local change Math preparation · 13 min
To explore28 Several inputs, several rates of change Math preparation · 9 min
To explore29 Choose a direction and a step size Math preparation · 9 min
To exploreChapter 5 · Lessons 30–36 Helping a model improve Explore helping a model improve; math preparation when it is needed. 7 lessons · 0 of 8 core ideas demonstrated
30
How does a model improve a prediction? Learning mechanisms · 32 min
To explore31 Probability without the mystery Math preparation · 10 min
To explore32 Averages over uncertain outcomes Math preparation · 9 min
To explore33 Learn from a sample of examples at each step Learning mechanisms · 11 min
To explore34 How momentum and Adam use earlier gradients Learning mechanisms · 15 min
To explore35 Constrain a solution or change the update Learning mechanisms · 12 min
To explore36 Powers and logarithms, one step at a time Math preparation · 11 min
To exploreChapter 6 · Lessons 37–43 Making sense of images Explore helping a model improve; inside a neural network; making sense of images; and supporting ideas. 7 lessons · 0 of 7 core ideas demonstrated
37
When the computer cannot store the exact number Math preparation · 12 min
To explore38 Keep signals and gradients in a usable range Learning mechanisms · 12 min
To explore39 Help information and learning signals travel Learning mechanisms · 12 min
To explore40 Build visual features from local operations Learning mechanisms · 14 min
To explore41 Locate objects and label their pixels Learning mechanisms · 14 min
To explore42 Different ways to learn Common Ground · 12 min
To explore43 Create a learning task from the data itself Learning mechanisms · 14 min
To exploreChapter 7 · Lessons 44–50 Learning to predict Explore making sense of images; learning to predict; learning from useful data; and supporting ideas. 7 lessons · 0 of 7 core ideas demonstrated
44
Learn visual features before choosing the final task Learning mechanisms · 13 min
To explore45 Turn a linear score into a class probability Learning mechanisms · 11 min
To explore46 Who is represented by the data? Learning mechanisms · 14 min
To explore47 Decide what success means before training Learning mechanisms · 12 min
To explore48 Look inside the average score Learning mechanisms · 14 min
To explore49 Probability, likelihood, and what is unknown Learning mechanisms · 11 min
To explore50 Update a belief using new evidence Learning mechanisms · 13 min
To exploreChapter 8 · Lessons 51–58 Learning from useful data Explore working with uncertainty; learning from useful data; learning to predict; and supporting ideas. 8 lessons · 0 of 8 core ideas demonstrated
51
Value a choice and its later consequences Math preparation · 12 min
To explore52 Turn a probability into a justified action Learning mechanisms · 11 min
To explore53 What can a small experiment tell us? Math preparation · 10 min
To explore54 Clean data without erasing the problem Learning mechanisms · 12 min
To explore55 Protect the examples used to judge a model Learning mechanisms · 15 min
To explore56 Control complexity without peeking at the answer Learning mechanisms · 12 min
To explore57 Evaluate the procedure that chose the model Learning mechanisms · 14 min
To explore58 Change one thing and measure what follows Learning mechanisms · 13 min
To exploreChapter 9 · Lessons 59–65 Testing what a model has learned Explore how computers represent a problem; testing what a model has learned; checking reliability and behavior; and supporting ideas. 7 lessons · 0 of 7 core ideas demonstrated
59
Repeat an experiment without confusing luck with truth Learning mechanisms · 11 min
To explore60 Make the result possible to inspect Learning mechanisms · 12 min
To explore61 Measure the behavior that the task actually needs Learning mechanisms · 14 min
To explore62 Decide whose problem the system solves and who bears its errors Learning mechanisms · 12 min
To explore63 Will the model work on new examples? Learning mechanisms · 14 min
To explore64 Test the rule where conditions change Learning mechanisms · 14 min
To explore65 Test what the visual model is actually using Learning mechanisms · 14 min
To exploreOptional extensions and comparisons (5)
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.Treat image patches as a sequence of representationsTreat an image as a sequence of patches: the transformer approach to vision.Infer structure that a flat image does not directly revealRecover depth, motion, and 3D shape from flat images: the geometry behind AR and self-driving perception.Ask what an explanation method actually measuresWhich inputs made the model decide? Integrated gradients, Shapley values, and the sanity check some saliency maps fail.