Google发布的第二代深度学习系统TensorFlow
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随着新代码、新项目和新章节的推出，第二版为读者提供了一个坚实的机器学习基础，并为读者提供了一个完整的学习概念。由NASA喷气推进实验室副首席技术官和首席数据科学家Chris Mattmann编写，所有的例子都伴随着可下载的Jupyter笔记本，以亲身体验用Python编写TensorFlow。新的和修订的内容扩大了核心机器学习算法的覆盖面，以及神经网络的进步，如VGG-Face人脸识别分类器和深度语音分类器。
https://www.manning.com/books/machine-learning-with-tensorflow-second-edition
使用TensorFlow的机器学习，第二版是使用Python和TensorFlow构建机器学习模型的完全指南。您将把核心ML概念应用于现实世界的挑战，如情感分析、文本分类和图像识别。实例演示了用于深度语音处理、面部识别和CIFAR-10自动编码的神经网络技术。
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【百度 提取码: wkqg】Machine Learning with TensorFlow, Second Edition
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Open source machine learning (ML) libraries allow developers to integrate advanced ML functionality into their own applications. However, popular ML libraries, such as TensorFlow, are not available natively in all programming languages and software package ecosystems. Hence, developers who wish to use an ML library which is not available in their programming language or ecosystem of choice, may need to resort to using a so-called binding library. Binding libraries provide support across programming languages and package ecosystems for a source library. For example, the Keras .NET binding provides support for the Keras library in the NuGet (.NET) ecosystem even though the Keras library was written in Python. In this paper, we conduct an in-depth study of 155 cross-ecosystem bindings and their development for 36 popular open source ML libraries. Our study shows that for most popular ML libraries, only one package ecosystem is officially supported (usually PyPI). Cross-ecosystem support, which is available for 25% of the studied ML libraries, is usually provided through community-maintained bindings, e.g., 73% of the bindings in the npm ecosystem are community-maintained. Our study shows that the vast majority of the studied bindings cover only a small portion of the source library releases, and the delay for receiving support for a source library release is large.
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