MATHEMATICS FOR MACHINE LEARNING

Calculus for Machine Learning

Calculus is vital in machine learning, providing the mathematical foundation for understanding and optimizing complex models. It enables us to analyze the behaviour of functions, find optimal solutions, and make accurate predictions. Without a solid understanding of calculus, it becomes challenging to comprehend the underlying principles and techniques used in machine learning algorithms.

Machine learning involves optimization, regression, classification, and neural network training. Calculus provides essential tools and concepts to address these tasks effectively. From gradient descent optimization to backpropagation in neural networks, calculus is the driving force behind many fundamental algorithms and methodologies in machine learning.

In this article, we will explore the role of calculus in machine learning and its applications in various tasks. The article will be organized into the following sections:

1. Derivatives
2. Partial Derivatives
3. Optimization in Machine Learning
   - Gradient Descent
   - Backpropagation
   - Higher-order Derivatives
4. Regression with a Perceptron
5. Classification with a Perceptron

Derivatives