# Mathematics for NN

#### 1. Introduction to the Mathematics of Neural Networks

1.1. Why is mathematics important?

1.2. The connection between mathematics and programming in neural networks

#### 2. Linear and Nonlinear Activation Functions

2.1. Linear functions

2.2. Nonlinear functions (ReLU, Leaky ReLU, Tanh, etc.)

2.3. Graphical analysis of activation functions (Sigmoid, ReLU, Softmax)

2.4 Loss Functions (Cross-Entropy, Mean Squared Error)

#### 3. Gradient Matrices and Backpropagation

3.1. The basic idea of backpropagation (Backpropagation and Weight Optimization)

3.2. Gradient computation using the chain rule

3.3. Examples in PHP

#### 4. Regularization in Neural Networks

4.1 What is overfitting?

4.2. Regularization methods (L1, L2, Dropout)

4.3. Mathematical justification for regularization

#### 5. Numerical Methods and Computation

5.1. Numerical stability in neural networks

5.2. The concept of vanishing and exploding gradients

5.3. Practical techniques for numerical optimization

#### 6. Entropy and Cross-Entropy

6.1 The concept of entropy

6.2. Cross-entropy loss function

6.3. Implementation of the loss function in PHP

#### 7. Conclusion: Combining Mathematics and Programming

7.1. How mathematics aids in building efficient neural networks

7.2. Recommendations for further study
