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.

在研究了一段时间的机器学习核心理论与实战后，总结一下学到的核心思想与实践方法 After spending some time learning the core theories and practical applications of machine learning, I’ve summarized the key concepts and practical methods I’ve learned.

介绍递归神经网络的原理、词向量原理和一个相关的影评情感分析的实际应用示例 Introduction to the principles of Recurrent Neural Networks (RNNs), word embeddings, and a practical example of sentiment analysis on movie reviews.

介绍卷积神经网络的原理、计算方法、相关参数和经典架构，以及一个图片分类的TensorFlow代码示例 An introduction to the principles of convolutional neural networks, their calculation methods, related parameters, and classic architectures, along with an image classification TensorFlow code example.

介绍神经网络的原理、各模块的一些概念，以及一个简单图片分类的TensorFlow代码示例 An Introduction of the principles of neural networks, the concepts of various modules, and a simple image classification code example using TensorFlow.

本节介绍无监督学习聚类算法K-means与DBSCAN原理，并使用代码进行实际场应用场景的分析 This section introduces the principles of unsupervised learning clustering algorithms, K-means and DBSCAN, and uses code to analyze real-world application scenarios.