[Verse 1] Vectors dance in n-dimensional space Matrices transform with algebraic grace Dot products whisper geometric truth While eigenvalues unlock the proof Tensors reshape our neural pathways Through computational maze [Chorus] Gradient descent slides down the slope Partial derivatives show us hope Chain rule cascades through every layer Loss functions guide our prayer Mathematics fuels the AI brain Where calculus meets the data chain [Verse 2] Bayes flips probabilities around Conditional chances can be found Distributions paint the likelihood While entropy measures understood Information theory counts the bits Where uncertainty never quits [Chorus] Gradient descent slides down the slope Partial derivatives show us hope Chain rule cascades through every layer Loss functions guide our prayer Mathematics fuels the AI brain Where calculus meets the data chain [Bridge] Batch by batch or stochastic leap Mini-batches find the balance sweet Cost function valleys call our name Optimization plays the game KL divergence measures distance Between predictions and existence [Verse 3] Python loops compute each gradient step While learning rates control the prep Regression problems teach us well How mathematics can excel From scratch we build what neural nets need Mathematical foundation seed [Chorus] Gradient descent slides down the slope Partial derivatives show us hope Chain rule cascades through every layer Loss functions guide our prayer Mathematics fuels the AI brain Where calculus meets the data chain [Outro] Linear algebra holds the key Calculus sets the data free Probability lights the way For artificial minds today
← Unit 1.1 — What Is AI? Taxonomy & History | Unit 1.3 — Python & AI Tooling Ecosystem →