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Full notes at ocdevel.com/mlg/8

• Linear Algebra: Essential for matrix operations; analogous to chopping vegetables in cooking. Every step of ML processes utilizes linear algebra.
• Statistics: The hardest part, akin to the cookbook; supplies algorithms for prediction and error functions.
• Calculus: Used in the learning phase (gradient descent), similar to baking; it determines the necessary adjustments via optimization.

• Recommendation: Learn the basics of machine learning first, then dive into necessary mathematical concepts to prevent burnout and improve appreciation.

• MOOCs: Khan Academy - Offers Calculus, Statistics, and Linear Algebra courses.
• Textbooks: Commonly recommended books for learning calculus, statistics, and linear algebra.
• Primers: Short PDFs covering essential concepts.

• The Great Courses: Offers comprehensive video series on calculus and statistics. Best used as audio for supplementing primary learning. Look out for "Mathematical Decision Making."

• Tensor: General term for any dimension list; TensorFlow from Google utilizes tensors for operations.
• Efficient computation using SimD (Single Instruction, Multiple Data) for vectorized operations.

• Gradient descent used for minimizing loss function, known as convex optimization. Recognize keywords like optimization in calculus context.