Grades 3–5
iiNNoo for Elementary School
Young students can discover that a computer's prediction is not magic. It is a number multiplied by another number. These lessons use one or two inputs, one output and whole numbers students can check on paper or in their heads.
Learning objectives
Students will be able to:
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01
Inputs become outputs
Describe a neural network as a machine that turns an input number into an output number, one step at a time.
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02
A weight is a multiplier
Explain that each connection multiplies the number passing along it, and that a bigger weight makes a bigger output.
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03
Predict, then check
Work out what the network will say before it says it, then step through the calculation to check.
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04
Tune the network
Change a weight until the network's predictions match what really happened.
Lessons to explore
Here are 6 lessons, in order. Each one opens up the app. You may also find all iiNNoo lessons here.
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The Zoo Visitors Predictor 01
This network predicts how many people visit a zoo based on the amount of sunshine. The input value is the hours of sunshine. The output value is the number of visitors. Step through the calculation for the first input. Observe how the weight affects the visitors, then repeat with the other input values.
Open lesson -
The Cricket Chirp Predictor
This network predicts how fast crickets chirp based on the temperature. The input value is the temperature. The output value is the chirps per minute. Crickets chirp about 6 times a minute for each degree, so the connection weight is 6. Before each output value is calculated, predict what it will be. Type your answer when asked.
Open lesson -
The Phone Battery Predictor
This network predicts phone battery usage when playing a video. The input value is the video length in hours. The output value is the percent of battery used. Edit the connection's weight so that the output values match the target outputs. When the numbers are close, the double lines will turn green. Test with all 4 inputs.
Open lesson -
The Butterfly Count Predictor
This network predicts the butterflies in a garden based on the number of flower beds and bushes. The input values are the flower beds and the bushes. The output value is the butterflies. Try to make each bush count twice as much as each flower bed. Edit the second weight to make this happen. Confirm your solution works for all inputs.
Open lesson -
The Tomato Harvest Predictor
This network predicts a garden's tomato harvest. The input values are the daily hours of sun and liters of water. The output value is the kilograms picked. Edit both connection weights so the output values match the target outputs. Hint: both weights are between 0 and 1.
Open lesson -
The Smoothie Stand Predictor
This network predicts a stand's sales based on the temperature. The input value is the temperature. The output values are the smoothies and popsicles sold. Edit both connection weights so that the output values match the target values. It takes 2 steps to calculate both outputs. Confirm your solution works for all inputs.
Open lesson
Example networks
Complete networks to run and explore freely, with no task to finish.
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The Tree Growth Predictor 01
This network predicts how tall a tree will be next year. The input value is the tree's height today. The output value is its height next year. The weight is the growth rate.
Open lesson -
The Lemonade Stand Predictor
This network predicts how many cups a lemonade stand sells. The input value is the temperature in degrees. The output value is the cups sold. The weight is how much each degree adds. The bias is what sells even at freezing. Edit either one and watch the output values change.
Open lesson -
The Honey Harvest Predictor
This network predicts how much honey a hive makes. The input values are the flowers growing nearby and the days in the month. The output value is the kilograms of honey. One weight is what each flower is worth, and the other is what each day of the month adds.
Open lesson
Grades 6–8
iiNNoo for Middle School
Middle schoolers are working with negative numbers, ratios and linear equations, and a neural network puts all three to work. These lessons add bias, activation functions and error, and end with a network that learns on its own.
Learning objectives
Students will be able to:
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01
Weight × input + bias
Model a real situation with a weight and a bias, and connect them to the slope and starting value of a linear equation, y = mx + b.
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02
Making a decision
Use negative weights and an activation function to show how a network decides, such as when to warn gardeners about frost.
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03
Measuring the error
Calculate error as the output value minus the target value, and explain what a positive or a negative error means.
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04
Learning from mistakes
Observe a network learning, as it nudges its weight and bias after every error to make the next error smaller.
Lessons to explore
Here are 9 lessons, in order. Each one opens up the app. You may also find all iiNNoo lessons here.
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The Zoo Visitors Predictor 02
This network predicts how many people visit a zoo based on the amount of sunshine. The input value is the hours of sunshine. The output value is the number of visitors. Right now, this network thinks each hour of sunshine brings 200 visitors. Edit the connection's weight so that each hour of sunshine brings 240 visitors.
Open lesson -
The Mountain Climb Calculator
This network predicts how the temperature changes as you climb a mountain. The input value is the kilometers climbed. The output value is the change in degrees. The air gets 6 degrees colder for every kilometer, so the weight is negative. Observe how a negative weight makes the output values negative.
Open lesson -
The Bus Fare Calculator 01
This network predicts the bus fare based on the number of miles traveled. The input value is the number of miles. The output value is the bus fare. It costs $2.00 to board the bus and $0.75 for each mile traveled. Observe how the bias adds the cost of boarding to every fare, then repeat with the other input values.
Open lesson -
The Ice Cream Sales Predictor
This network predicts ice cream sales based on the temperature outside. The input value is the temperature. The output value is the ice creams sold. None are sold below 60 degrees. Its activation function sets any negative value to zero after the bias is added. Observe how the activation function works as you step through the inputs.
Open lesson -
The Frost Warning Detector
This network warns gardeners about frost. The input value is the temperature. The output value is the warning level, which grows below 4 degrees. Edit the weight and bias so the output values match the target values. The activation function holds anything below zero at zero.
Open lesson -
The Bean Plant Predictor
This network predicts how tall a bean plant grows. The input value is the days since it sprouted. The output value is the height in cm. It also calculates the error: the output value minus the target value. Observe the error calculated for all 4 input values.
Open lesson -
The Arcade Ticket Predictor
This network predicts the tickets an arcade game pays out. The input value is the coins played. The output value is the tickets won. Luck decides the rest. Right now, every output value is too high, so every error is positive. Before each error is calculated, predict what it will be. Type your answer when asked.
Open lesson -
The Delivery Time Predictor 01
This network predicts how long a delivery takes. The input value is kilometers driven. The output value is minutes it takes. It also does something new. After each error, it updates the weight to make the error smaller. Observe the weight change over all 8 deliveries, each with its own traffic. This is our first learning network!
Open lesson -
The Cookie Jar Predictor
This network predicts how many cookies are left in the jar. The input value is the days since baking. The output value is the cookies left. The weight is already about right, but the bias, the cookies in a fresh jar, starts too low. Observe how the bias and the weight both change as you step through all 16 jars.
Open lesson
Example networks
Complete networks to run and explore freely, with no task to finish.
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The Tree Growth Predictor 02
This network predicts how tall a tree will be next year. The input value is the tree's height today. The output value is its height next year. This time the network learns the weight, the growth rate, by itself. Step through the training data and watch the weight go from 1 to 1.27, then try the testing data.
Open lesson -
The Spam Filter
This network tells spam from a real message. The input values are the links, the shouted words and the polite words in it. The output values are the spam and safe ratings. Links and shouting push one way, politeness the other. It may help to zoom out (-) to see all input / output pairs.
Open lesson -
The Recycling Sorter
This network tells glass from plastic. The input values are how heavy and how shiny a piece is. The output values are the glass and plastic ratings. Step through the training data and watch the two output values separate. The network picks whichever output value is larger.
Open lesson
Grades 9–12
iiNNoo for High School
High schoolers can follow the whole algorithm behind modern AI: forward propagation through hidden layers, back propagation of the error, and gradient descent. Every calculation stays on screen, so the network is never a black box.
Learning objectives
Students will be able to:
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01
Hidden layers
Trace a forward pass through hidden layers, with weighted sums, biases and an activation function, and predict each middle node's value before the network works it out.
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02
Back propagation
Follow the error back across each weight, see it add up at a node that feeds several outputs, and see it stop at a node the activation function held at zero.
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03
Gradient descent
Apply the update rule, input value × error × learning rate, and explain why a learning rate that is too large makes training overshoot or blow up.
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04
Training vs. testing
Use testing data the network has never seen to judge whether it has truly learned, and compare how well it learned each of its outputs.
Lessons to explore
Here are 9 lessons, in order. Each one opens up the app. You may also find all iiNNoo lessons here.
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The Talent Show Predictor
This network predicts the audience at a school talent show. The input values are the number of acts and posters put up. Each input value reaches both middle nodes, and both middle nodes reach the output value. In the first round, predict each middle node value, then the output value. Enter your answer when asked.
Open lesson -
The Birthday Book Predictor
On your birthday, you get money that you can use to buy books. The input value is the dollars and the output value is the books. You owe your friend $10, so the middle node's bias is -10. Each book costs $5. Predict the middle node, then the output value, in the first two rounds. The activation function holds anything below zero at zero.
Open lesson -
The Street Musician Predictor
This network predicts what a street musician collects. The input values are the hours played in the morning and in the evening. The output value is the money in the hat. The input values never receive an error, because nothing can change them. Predict the error that reaches the middle node, where it stops.
Open lesson -
The City Park Predictor
This network predicts the litter left in a city park. The input values are the food trucks and snack carts. The output values are the wrappers and bottles left behind. Each middle node collects the errors from both output values. Predict the error that reaches each middle node in the first two rounds.
Open lesson -
The Yawn Predictor
This network predicts a student's yawns in class. The input values are the hours slept and the hours needed. Too much sleep or too little brings yawns. At most one middle node is above zero each round, and only that one can receive an error. Predict the error that reaches each middle node in the first two rounds.
Open lesson -
The Pet Rescue Predictor
This network predicts a day at a pet rescue. The input values are the dogs and the cats waiting for homes. The output values are the visitors and the adoptions. The bottom middle node's bias holds it at zero every day. Predict the error that reaches the top and bottom middle nodes in the first two rounds.
Open lesson -
The Recycling Drive Predictor
This network predicts the bags of cans a school recycling drive collects. The input value is the number of classes taking part. The output error comes back to both middle nodes. In round 1, predict the output error, both middle errors, and the adjustment to the weight to the top middle node. The learning rate is 0.02.
Open lesson -
The Test Score Predictor 02
This network predicts a test score. The input values are the hours of study and of sleep. The output value is the score. Its weights blow up as it learns. Find a learning rate that keeps them under control. Figure out which learning rate works, then run it again.
Open lesson -
The Harbor Predictor
This network learns how busy a harbor will be, through a middle layer. The input values are the sunshine and the wind, from 0 to 5. The output values are the swimmers and sailboats. Decide which of the two answers it learns better. Train it on all the days, then switch to testing data and compare.
Open lesson
Example networks
Complete networks to run and explore freely, with no task to finish.
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The Traffic Light Decider
This network decides how long each of 3 roads gets a green light. The input values are the cars waiting on the north, east and south roads. The output values are the 3 green light times. Each road has its own path through a middle node. Click on a connection to see its weight.
Open lesson -
The Air Quality Monitor
This network turns 4 roadside readings into 4 pollutant levels. The input values are the traffic, wind, temperature and humidity, each scored from 0 to 5. The output values are the dust, nitrogen, ozone and carbon. With 255 steps a round, try swift mode with this one. Train it, then switch to testing data to confirm it learned correctly.
Open lesson -
The Next Word Predictor
This network predicts the next word in a sentence, like a tiny version of the AI that writes text. The input value is a word, and the output values show which word comes next. It learns from 7 short sentences, such as “the dog is cute” and “the sky is blue.” Train it in swift mode, then switch to testing data. Which words does it expect after “is”?
Open lesson
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