Neural Network

I. Combining biological inspiration with mathematics — overview of Neural Network

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flowchart LR
    A1["Simple data input"] -- "Non-linear activation and weighted summation" --> B1["Complex pattern recognition and inference"]
    style A1 fill:#f9f9f9,stroke:#333,stroke-width:1px
    style B1 fill:#e1f5fe,stroke:#01579b,stroke-width:1px

Definition: an artificial neural network algorithm that imitates how neurons operate in the human brain, learning non-linear patterns in data through multiple layers of nodes connected by weights ( Weights )

Characteristics: ( Non-linearity ) able to model complex non-linear relationships through activation functions ( Universal Approximator ) theoretically holds the universality ( Universal Approximator ) to approximate any complex function ( Parallel Processing ) a structure in which many computations can run simultaneously, making it well suited to GPU acceleration

II. Layer structure and core mechanisms of Neural Network

A. Signal propagation and the training process of a neural network

    graph TD
    A2["Data input"] --> B2["Forward Propagation"]
    B2 --> C2["Loss computation\n(Loss Function)"]
    C2 --> D2["Backpropagation"]
    D2 --> E2["Weight update\n(Optimizer)"]

B. Core components and detailed functions

Category	Key Element	Detailed Description
Neuron (Node)	Perceptron	The basic unit that takes an input, multiplies it by a weight, and adds a bias ( Bias )
Activation Function	Activation Function	Transforms the output signal to introduce non-linearity (e.g., ReLU, Sigmoid, Softmax)
Loss Function	Loss Function	Measures the difference between the true value and the predicted value (e.g., MSE, Cross-Entropy)
Backpropagation	Backpropagation	Propagates the error at the output layer back toward the input layer using the chain rule ( Chain Rule )
Optimization	Optimizer	Updates the weights to minimize loss (e.g., SGD, Adam, RMSprop)

III. Limitations and solutions of Neural Network

Item	Problem (Limit)	Solution
Vanishing Gradient	Vanishing Gradient	Use the ReLU activation function and apply Batch Normalization
Overfitting	Overfitting	Dropout, L1/L2 Regularization, Early Stopping
Black Box	Explainability	XAI (Explainable AI) techniques and attention ( Attention ) map analysis

Technology trends: as neural networks addressed the challenges that arise as they grow deeper, they evolved into Deep Learning, and today the Transformer architecture serves as the core underlying technology for large language models ( LLM )

Optimization Backpropagation