Google发布的第二代深度学习系统TensorFlow

VIP内容

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。

成为VIP会员查看完整内容
0
33

最新论文

Deep learning (DL) has been increasingly applied to a variety of domains. The programming paradigm shift from traditional systems to DL systems poses unique challenges in engineering DL systems. Performance is one of the challenges, and performance bugs(PBs) in DL systems can cause severe consequences such as excessive resource consumption and financial loss. While bugs in DL systems have been extensively investigated, PBs in DL systems have hardly been explored. To bridge this gap, we present the first comprehensive study to characterize symptoms, root causes, and introducing and exposing stages of PBs in DL systems developed in TensorFLow and Keras, with a total of 238 PBs collected from 225 StackOverflow posts. Our findings shed light on the implications on developing high performance DL systems, and detecting and localizing PBs in DL systems. We also build the first benchmark of 56 PBs in DL systems, and assess the capability of existing approaches in tackling them. Moreover, we develop a static checker DeepPerf to detect three types of PBs, and identify 488 new PBs in 130 GitHub projects.62 and 18 of them have been respectively confirmed and fixed by developers.

0
0
下载
预览
Top