Notes from Baidu Machine Learning Training Camp — Mathematical Fundamentals

This content mainly explains the basic concepts of neural networks and some important foundational concepts, including but not limited to algorithms such as linear regression and gradient descent, along with their principles and applications. Additionally, it provides detailed explanations of concepts like backpropagation and activation functions (e.g., Sigmoid, Tanh, and ReLU), and uses code examples for chart visualization. Below is a brief summary of these contents: 1. **Linear Regression**: A simple machine learning method used to predict continuous values. 2. **Gradient Descent**: One of the optimization algorithms, used to solve for parameters that minimize the loss function.