# AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
## 2 Introduction to Related Technologies
### XGBoost Model
#### 2.2.2 Deep Learning  
Machine learning methods can be broadly categorized into two major types: supervised learning and unsupervised learning. Supervised learning primarily encompasses classification and regression tasks, while unsupervised learning focuses on problems such as clustering and association analysis. Deep learning falls under the domain of supervised learning and is based on neural network technology, which traces its origins back to the 1950s and 1960s, when it was known as the perceptron. The perceptron consisted of an input layer, an output layer, and a single hidden layer. The input feature vectors were transformed through the hidden layer to produce classification results at the output layer [88]. Deep learning involves learning the intrinsic patterns and hierarchical representations of sample data. The knowledge acquired during this learning process significantly enhances the interpretation of data such as text, images, and audio. The ultimate goal of deep learning is to enable machines to develop human-like analytical and learning capabilities, allowing them to recognize and process textual, visual, and auditory information effectively [89]. As a sophisticated machine learning algorithm, deep learning has achieved remarkable breakthroughs, particularly in speech and image recognition, far surpassing previous technologies in these domains. It has also made significant advancements in various fields, including search technology, data mining, machine learning, machine translation, natural language processing, multimedia learning [90], speech processing, recommendation systems, and personalized technology. By enabling machines to emulate human sensory perception and cognitive processes, deep learning has addressed numerous complex pattern recognition challenges, thereby driving substantial progress in artificial-intelligence-related technologies [91]. The fundamental models in deep learning can be broadly classified into three categories: multilayer perceptron models, deep neural network models, and recurrent neural network models. Representative architectures for these categories include Deep Belief Networks for MLP models, Convolutional Neural Networks for DNN models, and Recurrent Neural Networks for sequential data processing [92]. Table 9 compares several mainstream deep learning algorithms in recent years, including CNNs, RNNs, LSTM, GRU, Transformer, GANs, and Variational Autoencoders. CNNs are primarily used for computer vision tasks, leveraging convolutional operations to extract features while improving computational efficiency through parameter sharing. RNNs and their variants, LSTM and GRU, are designed for sequential data processing, with LSTM mitigating the vanishing gradient problem via gating mechanisms, whereas GRU achieves similar performance with reduced computational complexity [93]. Transformers employ self-attention mechanisms to enhance long-range dependency modeling, making them highly effective in natural language processing tasks. GANs utilize adversarial training to generate high-quality data but are prone to mode collapse during training. VAEs, based on probabilistic modeling, produce coherent data distributions with stable training, though the generated samples may lack fine details compared to GANs [94].