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.
Lesson plans
Ready-made plans for a day, three days or a week, with what to say, what to watch for and what to ask afterwards.
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The Equation Inside an AI
A single class that finds y = mx + b inside a neural network. Students change a weight, watch a bias add a starting value, see an activation function hold a value at zero, then set a weight and a bias themselves.
Open lesson plan -
How a Network Decides and Learns
Three days that take a network from an equation to a learner: weights and bias, then activation functions that make a decision, then error and the first network that fixes itself. Each day starts with a short whole-class introduction and ends with a discussion.
Open lesson plan -
Teaching a Network to Learn
A week-long unit that connects neural networks to the linear equations students are learning, and ends with networks that learn on their own. Every lesson on the middle school page is used, along with its three example networks.
Open lesson plan
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
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