Basic Math Concepts – First and Second Derivatives in Neural Networks

To understand and implement derivatives in neural networks, one should know:

1. Single-variable calculus: Derivatives of basic functions
2. Partial derivatives: Needed for multivariable functions (neural nets have many weights)
3. Chain rule: Essential for backpropagation
4. Matrix calculus (for second order):

  • Jacobians (for gradients)
  • Hessians (for curvature)

5. Optimization basics: Gradient descent and Newton’s method

First and Second Derivatives in Neural Networks – Visual Roadmap