# Spotify represents every song as ~12 numbers
billie_eilish_bad_guy = [
    0.70,   # danceability (0-1)
    0.43,   # energy (0-1)  
    0.56,   # speechiness (0-1)
    0.32,   # acousticness (0-1)
    0.00,   # instrumentalness (0-1)
    0.36,   # liveness (0-1)
    0.68,   # valence/happiness (0-1)
    135.0,  # tempo (BPM)
    # ... more features
]

# This list IS a vector. That's it. A vector is just a list of numbers.

# A 100x100 pixel selfie = 10,000 numbers (brightness of each pixel)
# A neural network can compress this to just 128 numbers that capture "you-ness"

your_face_embedding = [0.23, -0.45, 0.89, ..., 0.12]  # 128 numbers

# Similar faces have similar numbers!

# Every movie can be described by hidden factors
inception = [
    0.95,   # "mind-bending" factor
    0.80,   # "action" factor  
    0.20,   # "romance" factor
    0.60,   # "visual spectacle" factor
    # ...
]

# Create a new environment and install what we need
pip install numpy matplotlib jupyter scikit-learn pillow plotly ipywidgets

# Start Jupyter to follow along
jupyter notebook

import numpy as np

# Three songs represented as vectors [energy, danceability, acousticness]
blinding_lights = np.array([0.73, 0.51, 0.00])  # The Weeknd
levitating = np.array([0.69, 0.70, 0.03])        # Dua Lipa
someone_like_you = np.array([0.34, 0.50, 0.75])  # Adele

def similarity(song_a, song_b):
    """Cosine similarity - how similar are two vectors?"""
    return np.dot(song_a, song_b) / (np.linalg.norm(song_a) * np.linalg.norm(song_b))

print(f"Blinding Lights vs Levitating: {similarity(blinding_lights, levitating):.2f}")
print(f"Blinding Lights vs Someone Like You: {similarity(blinding_lights, someone_like_you):.2f}")

# Output:
# Blinding Lights vs Levitating: 0.94  (very similar! both are upbeat pop)
# Blinding Lights vs Someone Like You: 0.54  (less similar - different vibes)