AI & MACHINE LEARNING PROGRAM • LEVEL 06 — LINEAR REGRESSION

Perform a Gradient Descent Step with Python

Learn perform a gradient descent step with python with a short, executable Python example.

IntermediateGradient descentLearning rate

PROBLEM UNDERSTANDING

Input and expected output

Sample input

No input required

Sample output

2.2

COMPLETE PYTHON PROGRAM

Complete Python implementation

x, y = 2, 5
weight, learning_rate = 1.0, 0.1
prediction = weight * x
gradient = 2 * x * (prediction - y)
weight -= learning_rate * gradient
print(round(weight, 2))

CURRENT STEP

Select Start to walk through the important lines.

SELECTED LINE

No line selected

EXPECTED OUTPUT FOR THE SAMPLE

2.2

0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

Initialize the sample values used to perform a gradient descent step.
Apply Gradient descent and Learning rate to compute the required result.
Display the result for perform a gradient descent step and compare it with the documented sample output.

This example of perform a gradient descent step computes the result directly from the prepared sample data. It demonstrates Gradient descent and Learning rate and prints a deterministic result that can be checked against the sample output.

EFFICIENCY

Time and space complexity

Time complexity

O(n)

Auxiliary space

O(1)

DEBUGGING CHECKLIST

Common mistakes

Check this

For perform a gradient descent step, keep the data shape and value types consistent with Gradient descent.

Check this

Apply Gradient descent in the same order shown by the algorithm; changing the order can change the result.

Check this

Verify the final Gradient descent and Learning rate result against the sample before trying new data.

Try it yourself

Practice: Run the program with the sample input, predict its output, and then test one boundary case of your own.