《学习PyTorch 2.0 第二版》：利用PyTorch 2.3和CUDA 12进行神经网络和深度学习模型的实验

English | 2024 | 8119177916 | 191 pages| Epub PDF (both convert) | 1 MB

"Learning PyTorch 2.0, Second Edition" is a fast-learning, hands-on book that emphasizes practical PyTorch scripting and efficient model development using PyTorch 2.3 and CUDA 12. This edition is centered on practical applications and presents a concise methodology for attaining proficiency in the most recent features of PyTorch. The book presents a practical program based on the fish dataset which provides step-by-step guidance through the processes of building, training and deploying neural networks, with each example prepared for immediate implementation. Given your familiarity with machine learning and neural networks, this book offers concise explanations of foundational topics, allowing you to proceed directly to the practical, advanced aspects of PyTorch programming. The key learnings include the design of various types of neural networks, the use of torch.compile() for performance optimization, the deployment of models using TorchServe, and the implementation of quantization for efficient inference. Furthermore, you will also learn to migrate TensorFlow models to PyTorch using the ONNX format. The book employs essential libraries, including torchvision, torchserve, tf2onnx, onnxruntime, and requests, to facilitate seamless integration of PyTorch with production environments. Key Learnings Master tensor manipulations and advanced operations using PyTorch's efficient tensor libraries. Build feedforward, convolutional, and recurrent neural networks from scratch. Implement transformer models for modern natural language processing tasks. Use CUDA 12 and mixed precision training (AMP) to accelerate model training and inference. Deploy PyTorch models in production using TorchServe, including multi-model serving and versioning. Migrate TensorFlow models to PyTorch using ONNX format for seamless cross-framework compatibility. Optimize neural network architectures using torch.compile() for improved speed and efficiency. Utilize PyTorch's Quantization API to reduce model size and speed up inference.

《PyTorch 2.0，第二版》是一本快速学习、动手实践的书籍，专注于利用PyTorch 2.3和CUDA 12高效编写脚本和开发模型。该版本以实用应用为中心，并为掌握PyTorch最新特性的简明方法提供了介绍。本书基于鱼类数据集介绍了一个实际程序，通过构建、训练和部署神经网络的过程提供逐步指导，每个示例都准备好了立即实施。鉴于您对机器学习和神经网络的理解，《这本书》提供了PyTorch基础主题的简洁解释，让您能够直接进入PyTorch编程的实用与高级部分。 关键学习内容包括使用PyTorch高效张量库设计各种类型的神经网络、使用torch.compile()进行性能优化、通过TorchServe部署模型以及实施量化以提高推理效率。此外，您还将学习如何将TensorFlow模型迁移到PyTorch，使用ONNX格式实现无缝跨框架兼容性。该书采用的必要库包括torchvision、torchserve、tf2onnx、onnxruntime和requests，以便于PyTorch与生产环境之间的无缝集成。 关键学习内容 掌握使用PyTorch高效张量库进行张量操作和高级操作。 从头构建前馈型、卷积型和递归型神经网络。 实现Transformer模型以处理现代自然语言处理任务。 利用CUDA 12和混合精度训练（AMP）加速模型训练和推理。 通过TorchServe在生产环境中部署PyTorch模型，包括多模型服务和版本控制。 使用ONNX格式将TensorFlow模型迁移到PyTorch，以便实现无缝跨框架兼容性。 利用torch.compile()优化神经网络架构以提高速度和效率。 使用PyTorch的量化API减少模型大小并加快推理速度。

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