深度学习入门指南:项目实战与生产部署

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《深度学习实战手册》(2018版)是一本实用指南,作者是Douwe Osinga,专为初学者提供快速入门的实践菜谱。该书结构清晰,分为多个章节,旨在帮助读者理解和应用深度学习技术。 第一章深入介绍了神经网络的工作原理,包括数据来源以及如何预处理数据以提高模型的处理效率。这对于理解深度学习的基础至关重要,因为数据质量和预处理质量直接影响到模型的性能。 第二章关注在项目实施过程中可能遇到的问题和解决策略。由于神经网络具有较强的复杂性,调试往往不易,书中提供的技巧将有助于读者在后续章节中的项目实践中克服困难。即使在初次阅读时跳过这一章,当遇到问题时可以回溯参考。 第三到十五章围绕不同的媒体类型展开,按照文本处理、图像处理和音乐处理的顺序组织。每个章节都围绕一个项目进行,包括数据获取、逐步构建模型的过程,以及数据可视化等内容,让读者通过实际操作掌握不同领域的深度学习应用。 第十六章重点讨论将模型部署到生产环境中的问题。作者引导读者了解如何在笔记本上进行实验后,将结果推向实际用户,使模型能在服务器或移动设备上运行。这章涵盖了将深度学习应用于现实场景的各种选项和注意事项,确保模型的可扩展性和可靠性。 《深度学习实战手册》不仅提供了丰富的理论知识,更注重实践经验的分享,适合希望在深度学习领域快速入门并提升技能的读者。无论是对于研究者、开发者还是对深度学习感兴趣的爱好者,这本书都是一个宝贵的参考资料和工具。
2018-07-25 上传
While the boom in computational power and better techniques led to an increase in interest in neural networks, we have also seen huge strides in usability. In particular, deep learning frameworks like TensorFlow, Theano, and Torch allow nonexperts to construct complex neural networks to solve their own machine learning problems. This has turned a task that used to require months or years of handcoding and head-on-table-banging effort (writing efficient GPU kernels is hard!) into something that anyone can do in an afternoon (or really a few days in practice). Increased usability has greatly increased the number of researchers who can work on deep learning problems. Frameworks like Keras with an even higher level of abstraction make it possible for anyone with a working knowledge of Python and some tools to run some interesting experiments, as this book will show. A second important factor for “why now” is that large datasets have become available for everybody. Yes, Facebook and Google might still have the upper hand with access to billions of pictures, user comments, and what have you, but datasets with millions of items can be had from a variety of sources. In Chapter 1 we’ll look at a variety of options, and throughout the book the example code for each chapter will usually show in the first recipe how to get the needed training data. At the same time, private companies have started to produce and collect orders of magnitude more data, which has made the whole area of deep learning suddenly commercially very interesting. A model that can tell the difference between a cat and a dog is all very well, but a model that increases sales by 15% by taking all historic sales data into account can be the difference between life and death for a company.