microsoft/AI-For-Beginners
Publicmirrored fromhttps://github.com/microsoft/AI-For-BeginnersAvailable
examples/01-hello-ai-world.py
138lines · modecode
| 1 | """ |
| 2 | Hello AI World - Your First AI Program |
| 3 | ======================================= |
| 4 | |
| 5 | This is a simple pattern recognition example that demonstrates core AI concepts: |
| 6 | - Learning from data |
| 7 | - Making predictions |
| 8 | - Understanding patterns |
| 9 | |
| 10 | What this program does: |
| 11 | - Learns a simple mathematical pattern (y = 2x) |
| 12 | - Uses that pattern to make predictions |
| 13 | - No complex libraries needed - just pure Python! |
| 14 | |
| 15 | Perfect for understanding AI basics before diving into neural networks. |
| 16 | """ |
| 17 | |
| 18 | import random |
| 19 | |
| 20 | class SimpleAILearner: |
| 21 | """ |
| 22 | A very simple AI that learns linear relationships. |
| 23 | This demonstrates the fundamental concept of AI: learning from data. |
| 24 | """ |
| 25 | |
| 26 | def __init__(self): |
| 27 | # The "weight" is what our AI learns |
| 28 | # It starts with a random guess |
| 29 | self.weight = random.uniform(0, 5) |
| 30 | self.learning_rate = 0.01 # How fast our AI learns |
| 31 | |
| 32 | def predict(self, x): |
| 33 | """ |
| 34 | Make a prediction based on what we've learned. |
| 35 | |
| 36 | Args: |
| 37 | x: Input value |
| 38 | |
| 39 | Returns: |
| 40 | Predicted output |
| 41 | """ |
| 42 | return self.weight * x |
| 43 | |
| 44 | def train(self, training_data, epochs=100): |
| 45 | """ |
| 46 | Train the AI to learn the pattern in the data. |
| 47 | |
| 48 | Args: |
| 49 | training_data: List of (input, output) pairs |
| 50 | epochs: Number of times to go through all the data |
| 51 | """ |
| 52 | print("🎓 Training started...") |
| 53 | print(f"Initial guess for weight: {self.weight:.2f}") |
| 54 | |
| 55 | for epoch in range(epochs): |
| 56 | total_error = 0 |
| 57 | |
| 58 | # Learn from each example |
| 59 | for x, y_actual in training_data: |
| 60 | # Make a prediction |
| 61 | y_predicted = self.predict(x) |
| 62 | |
| 63 | # Calculate error (how wrong we were) |
| 64 | error = y_actual - y_predicted |
| 65 | total_error += abs(error) |
| 66 | |
| 67 | # Update our weight to reduce error (this is learning!) |
| 68 | self.weight += self.learning_rate * error * x |
| 69 | |
| 70 | # Print progress every 20 epochs |
| 71 | if (epoch + 1) % 20 == 0: |
| 72 | avg_error = total_error / len(training_data) |
| 73 | print(f"Epoch {epoch + 1}/{epochs} - Average error: {avg_error:.4f} - Weight: {self.weight:.2f}") |
| 74 | |
| 75 | print(f"✅ Training complete! Final weight: {self.weight:.2f}") |
| 76 | |
| 77 | |
| 78 | def main(): |
| 79 | """ |
| 80 | Main function - Let's teach our AI! |
| 81 | """ |
| 82 | print("=" * 60) |
| 83 | print("Welcome to Hello AI World!") |
| 84 | print("=" * 60) |
| 85 | print() |
| 86 | print("Today, we'll teach an AI to learn a simple pattern:") |
| 87 | print("Given x, predict y where y = 2x") |
| 88 | print() |
| 89 | |
| 90 | # Step 1: Create training data |
| 91 | # The pattern we want the AI to learn: y = 2 * x |
| 92 | print("📊 Creating training data...") |
| 93 | training_data = [ |
| 94 | (1, 2), # When x=1, y should be 2 |
| 95 | (2, 4), # When x=2, y should be 4 |
| 96 | (3, 6), # When x=3, y should be 6 |
| 97 | (4, 8), # When x=4, y should be 8 |
| 98 | (5, 10), # When x=5, y should be 10 |
| 99 | ] |
| 100 | print(f"Training examples: {training_data}") |
| 101 | print() |
| 102 | |
| 103 | # Step 2: Create and train our AI |
| 104 | ai = SimpleAILearner() |
| 105 | ai.train(training_data, epochs=100) |
| 106 | print() |
| 107 | |
| 108 | # Step 3: Test our AI with new data |
| 109 | print("🧪 Testing our AI with new inputs...") |
| 110 | print("-" * 60) |
| 111 | test_inputs = [6, 7, 10, 15] |
| 112 | |
| 113 | for x in test_inputs: |
| 114 | prediction = ai.predict(x) |
| 115 | actual = 2 * x # The true answer |
| 116 | print(f"Input: {x:2d} | Prediction: {prediction:6.2f} | Actual: {actual:6.2f} | Difference: {abs(prediction - actual):.2f}") |
| 117 | |
| 118 | print("-" * 60) |
| 119 | print() |
| 120 | |
| 121 | # Explanation |
| 122 | print("💡 What just happened?") |
| 123 | print("1. We gave the AI examples of the pattern (y = 2x)") |
| 124 | print("2. The AI learned by adjusting its 'weight' to minimize errors") |
| 125 | print("3. After training, it can predict outputs for new inputs!") |
| 126 | print() |
| 127 | print("🎉 Congratulations! You just trained your first AI!") |
| 128 | print() |
| 129 | print("🚀 Next steps:") |
| 130 | print(" - Try changing the training data to learn different patterns") |
| 131 | print(" - Experiment with the learning_rate (line 29)") |
| 132 | print(" - Modify epochs to see how training time affects accuracy") |
| 133 | print() |
| 134 | |
| 135 | |
| 136 | if __name__ == "__main__": |
| 137 | # This runs when you execute the script |
| 138 | main() |
| 139 | |