快速入门深度学习:实用配方与实战项目

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《深度学习实战食谱:快速入门指南》(Deep Learning Cookbook: Practical Recipes to Get Started Quickly)是一本由Douwe Osinga撰写的实用手册,专为那些想要踏入深度学习领域但缺乏机器学习背景的软件工程师设计。该书旨在消除对深度学习的恐惧感,利用现代框架如Keras和TensorFlow,让读者能够迅速上手解决实际问题。 书中包含了一系列精心编排的项目,每个章节围绕一个具体任务展开,例如训练音乐推荐系统、利用词嵌入计算文本相似度、构建基于维基百科链接的电影推荐系统等。作者还提供了技巧性章节,帮助读者在遇到困难时找到解决方案,确保实践过程中的指导与支持。所有示例都采用Python编写,并通过GitHub上的Python笔记本形式提供代码供读者参考。 通过阅读本书,读者将学会如何开发服务于真实用户的应用程序,探索AI如何理解世界并可视化它们的内部状态,构建能够根据文本提出表情符号的模型,以及利用预训练网络构建反向图像搜索服务。此外,书中还将对比生成对抗网络(GANs)、自编码器(Autoencoders)和长短时记忆网络(LSTMs)在生成图标方面的表现,以及如何识别音乐风格并索引歌曲库。 作者Douwe Osinga凭借丰富的经验和深入浅出的讲解,使读者能够不仅掌握理论知识,还能将其转化为实际操作能力。无论你是初学者还是经验丰富的开发者,这本书都是提升深度学习技能、加速项目开发的理想工具。同时,书中提供的最新修订历史记录确保了读者能够获取到最新的技术更新和改进。 《深度学习实战食谱》是一本既适合自学也适合作为团队培训教材的书籍,它用实例驱动的方式引导读者步入深度学习的实践之旅,助力读者在这个快速发展的领域中取得成功。
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.