While I have a strong mathematical background in calculus and linear algebra, including the ability to perform matrix operations and transformations by hand, I recently discovered a crucial gap in my understanding: the geometric interpretation of vectors and their operations.

This realization came as I began exploring geometric explanations of linear algebra concepts. What were previously abstract lists of numbers or array indices suddenly transformed into intuitive geometric objects—arrows pointing to specific locations in space, representing directions and magnitudes in a visually comprehensible way.

Why Geometric Intuition Matters

Understanding vectors geometrically fundamentally changes how we approach machine learning problems:

  • Feature Spaces: Each data point becomes a position in high-dimensional space, making concepts like similarity and distance intuitive
  • Transformations: Matrix operations become geometric transformations—rotations, scalings, and projections
  • Optimization: Gradient descent transforms from abstract calculus into following the steepest downhill path in a landscape
  • Embeddings: Word embeddings and latent representations become geometric relationships where similar concepts cluster together

This geometric perspective provides deeper insight into why certain machine learning algorithms work and how to debug them when they don’t.

For anyone working in machine learning who hasn’t yet explored the geometric foundations, I highly recommend these courses:

Linear Algebra

  • Essence of Linear Algebra by 3Blue1Brown - Course Link An exceptional visual introduction to linear algebra concepts with stunning animations that build geometric intuition

  • Mathematics for Machine Learning: Linear Algebra - Coursera Specifically designed for ML practitioners, covering the essential linear algebra needed for understanding modern ML algorithms

Calculus and Optimization

  • Mathematics for Machine Learning: Multivariate Calculus - Coursera Focuses on the calculus concepts most relevant to machine learning, particularly gradient-based optimization

Dimensionality Reduction

  • Mathematics for Machine Learning: PCA - Coursera Explores Principal Component Analysis from both mathematical and geometric perspectives

Practical Impact

This geometric understanding has practical implications for machine learning work:

  • Better Model Design: Understanding how transformations affect data helps in designing better architectures
  • Debugging: Geometric intuition makes it easier to identify why models fail on certain data
  • Feature Engineering: Knowing how features interact geometrically guides better feature design
  • Interpretability: Geometric perspective aids in explaining model decisions to non-technical stakeholders

Investing time in building this geometric intuition is one of the most valuable things you can do as a machine learning practitioner. It transforms machine learning from a collection of algorithms and formulas into an intuitive, visual discipline where you can reason about what models are doing and why.