Understand the fundamental calculus concepts required for machine learning. This course covers derivatives, gradients, and optimization techniques used in algorithms like gradient descent and backpropagation. Gain practical understanding through examples relevant to AI engineers.
Prerequisites Algebra & basic Python helpful
Level:
Differential Calculus Fundamentals
Understand limits, derivatives, and differentiation rules for single-variable functions.
Calculus in Optimization
Apply derivatives to find function minima/maxima, forming the basis for model optimization.
Multivariable Calculus Concepts
Grasp partial derivatives, gradients, and the Hessian matrix for functions with multiple inputs.
Gradient-Based Optimization
Understand the mechanics of gradient descent and its variants used in training machine learning models.
Calculus in Neural Networks
Recognize the role of the chain rule in the backpropagation algorithm for training deep learning models.
Practical Application
Implement calculus concepts using Python libraries like NumPy for ML-related tasks.