文件名称:
Deep Learning Cookbook
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文件大小: 9mb
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上传时间: 2018-07-25
详细说明： 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 r eally 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. ...展开收缩
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