Chapter 7 of 'Efficient Deep Learning Book' discusses the importance of automation in deep learning, particularly in the context of technique selection, hyperparameter optimization, and neural architecture search. The chapter highlights how automation can simplify tedious processes, such as choosing between quantization and clustering, and how frameworks like Keras Tuner and Optuna facilitate hyperparameter tuning. It also introduces AutoML, which aims to automate most machine learning pipeline steps, reducing the need for expert intervention. The chapter concludes with examples of controller networks used in neural architecture search, such as those designed by Zoph et al., which predict network architectures and parameters.

The chapter focuses on advanced learning techniques in deep learning, particularly emphasizing self-supervised learning and its effectiveness in reducing reliance on labeled data. It introduces contrastive learning frameworks like SimCLR, which create positive pairs through data augmentation and projection heads to optimize representation similarity. The chapter also explores subclass distillation, which improves model accuracy in tasks with fewer classes. Additional techniques such as label smoothing, curriculum learning, and stochastic depth are discussed to address issues like overfitting and vanishing gradients. These methods aim to enhance model quality without increasing model footprint. Experiments and practical applications are recommended to understand the efficacy of these techniques in real-world scenarios.

This chapter discusses advanced compression techniques for deep learning models, focusing on clustering, sparsity, and quantization. Clustering involves grouping tensor elements into centroids to reduce storage costs, with the compression ratio depending on the number of centroids. Sparsity techniques, such as setting small weights to zero, can improve compression ratios and model efficiency. Quantization is compared to clustering, highlighting that clustering can better handle data distributions. The chapter also includes practical examples, such as compressing Mars Rover transmission data and implementing sparse models, demonstrating significant reductions in model size after applying clustering and sparsity techniques.

文档讨论了多种高效深度学习架构，包括Transformer的优化方法如低秩和内核近似，以及Depthwise Separable Convolution用于减少卷积计算量。还涉及了嵌入表的生成与应用，使用支持向量机进行分类，以及注意力机制在序列数据处理中的优势。

本章讨论了多种深度学习模型的学习技巧，以提高模型的效率和性能。文中详细介绍了标签效率和样本效率的概念，并通过实例说明了如何通过这些技术降低训练成本和所需数据量。数据增强技术如亮度变换、通道强度平移、空间变换等被详细探讨，展示了它们对模型性能的提升。此外，知识蒸馏方法被提出，用以从大的教师模型中提取知识，训练更小的学生网络。通过这些技术，可以在减少资源消耗的同时，保持或提高模型性能。例如，通过效率提升，一个300KB的模型可以用更少的标签和训练步骤达到相同的准确率。

The chapter discusses compression techniques in deep learning, focusing on quantization as a method to reduce model size while maintaining accuracy. Quantization involves converting high-precision values to lower-precision discrete values, trading off precision for smaller model size and faster inference. The chapter demonstrates quantization through examples such as image compression for Mars Rover transmission and handwritten digit recognition using the MNIST dataset. A quantized model achieved nearly identical accuracy to the original floating-point model while being 4X smaller. The chapter also explores the trade-offs between compression and quality, highlighting the practical benefits of quantization for deploying models in resource-constrained environments like mobile devices.

The chapter introduces the concept of efficient deep learning, emphasizing the need for balancing quality metrics (e.g., accuracy, precision) and footprint metrics (e.g., model size, latency). It discusses core efficiency techniques such as compression, learning methods, automation, and efficient model architectures. The chapter also highlights the importance of infrastructure and hardware in achieving efficiency. The book aims to provide tools and techniques to help practitioners make informed trade-offs between model quality and deployment efficiency.

文档阐述了人工智能发展史中的关键事件和技术进展。反向传播算法（Backpropagation）的发展历程是核心内容，从20世纪60年代的起源，到1970年首次计算机实现，再到1982年正式发表。多层感知机（MLP）的训练问题通过链式法则得以解决。1992年Support Vector Machine（SVM）的出现标志着一个重要里程碑。文档还提到了人工智能研究中的“暗时间”，包括论文被拒、Hinton转投CIFAR寻求资金，以及将“神经网络”重新品牌为“深度学习”的尝试。

文档详细记录了PyTorch多个版本的发布说明，包括18.04到18.11等版本的更新内容。每个版本更新包括了PyTorch容器镜像的改进、CUDA、cuDNN、NCCL、DALI等库的版本升级，以及新功能的引入，如多GPU性能优化、混合精度训练支持、JIT改进等。此外，文档还提供了Tensor Core优化的示例，包括ResNet50和GNMT v2模型的实现。已知问题部分提到了与持久批归一化相关的预测能力下降问题，并提供了相应的解决方案。

文档系统介绍了PyTorch框架的基础知识和使用方法，包括PyTorch的主要特性、张量操作、数据集与数据加载器、神经网络模型、损失函数、优化方法以及模型的保存与加载。文档还提到了PyTorch在自然语言处理、计算机视觉和语音处理等领域的应用，并推荐了一些使用PyTorch的GitHub仓库和参考资料。