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Topics

Common Mathematical Tools

• Taylor Expansion, SVD, Pseudoinverse, Gradient Descent, Backpropagation

Study Resources & Scope Used to Study

[1] Mathematics for Machine Learning - Deisenroth, Faisal, and Ong

• Vector Calculus
  • 5.2 Differentiation of Univariate Functions (Taylor Expansion)
  • 5.6 Backpropagation and Automatic Differentiation
• Matrix Decompositions
  • 4.5 Singular Value Decomposition (SVD)
  • 4.6 Matrix Approximation (Pseudoinverse)
• Continuous Optimization
  • 7.1 Optimization Using Gradient Descent

[2] Linear Algebra, 5th Edition - Friedberg, Insel, and Spence

• Inner Product Spaces
  • 6.7 The Singular Value Decomposition and the Pseudoinverse

[3] Deep Learning - Goodfellow, Bengio, and Courville

• Numerical Computation
  • 4.3 Gradient-Based Optimization (Gradient Descent)
• Machine Learning Basics
  • 5.9 Backpropagation and Other Differentiation Algorithms

[4] Python/NumPy/PyTorch Implementations

• Code for computing Taylor series approximations, SVD, pseudoinverse, gradient descent, and backpropagation in neural networks.