使用Docker搭建HDFS+Flink+Paimon流批一体环境，进行数据查询，并加入StarRocks查询 Using Docker to Set Up an HDFS + Flink + Paimon Unified Streaming and Batch Environment for Data Querying, Adding StarRocks For Data Querying

本章介绍了PyTorch的基础知识，包括张量、前向传播、反向传播等概念，以及 如何构建神经网络、优化数据加载、使用GPU训练模型等操作，为进行深度学习奠定基础 This chapter introduces the fundamentals of PyTorch, including concepts such as tensors, forward propagation, and backpropagation. It also covers how to build neural networks, optimize data loading, and train models using GPUs, laying the foundation for deep learning.

作为LLM应用的一种，我们可以将预训练好的模型进一步微调，使其具有多用途性并能完成输入的指令 As an application of LLMs, we can further fine-tune the pre-trained model to make it more versatile and capable of completing given instructions.

作为LLM应用的一种，我们可以将预训练好的模型进一步微调，使其能完成专业的垃圾邮件分类任务 As one application of LLMs, we can further fine-tune a pre-trained model to enable it to perform specialized tasks, such as spam classification.

构建损失函数实现对GPT模型的评估，实现预训练过程，文本生成的多样性策略以及加载开源的GPT2权重 Construct a loss function to evaluate the GPT model, implement the pre-training process, develop strategies for text generation diversity, and load open-source GPT-2 weights.

实现一个包含了多头注意力和前馈网络的Transformer块，并将其与token化与logits映射连接起来，实现可预测词功能 Implement a Transformer block that includes multi-head attention and feed-forward networks, and connect it with tokenization and logits mapping to enable word prediction functionality.