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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-pipelines-tensorflow
课程评论：没有评论
课程名称：使用TensorFlow数据服务构建数据管道 课程概述：将机器学习模型应用于现实世界不仅仅是进行建模。本专业课程将教授您如何驾驭各种部署场景，并更有效地使用数据来训练模型。在这第三门课程中，您将学习到： - 使用TensorFlow数据服务执行高效的ETL任务 - 通过TensorFlow Hub和TensorFlow数据服务API加载不同的数据集和自定义特征向量 - 创建和使用预构建的管道，为任何数据集生成高度可重复的输入/输出管道 - 优化在训练过程中可能成为瓶颈的数据管道 - 将自己的数据集发布至TensorFlow Hub库，与全球研究人员和开发者分享标准化数据 本专业课程建立在我们的TensorFlow实践专业课程基础之上。如果您是TensorFlow的新手，建议先学习TensorFlow实践专业课程。为了更深入地理解神经网络的工作原理，建议您修读深度学习专业课程。 大纲： 第1部分：使用TensorFlow数据服务构建数据管道 描述：本周，您将能够使用TensorFlow数据服务API执行高效的ETL任务。 第2部分：TensorFlow中的数据集分割与切片API 描述：本周，您将使用分割API构建任意数据集（无论是自定义的还是在TensorFlow Hub数据集库中的）的训练/验证/测试分割。 第3部分：将数据导出到训练管道 描述：本周，您将扩展对数据管道的理解。 第4部分：性能 描述：您将学习如何处理数据输入，以避免瓶颈、竞争条件等问题！
Part: 1
Title:Data Pipelines with TensorFlow Data Services
Description:This week, you will be able to perform efficient ETL tasks using Tensorflow Data Services APIs
Part: 2
Title:Splits and Slices API for Datasets in TF
Description:In this week, you will construct train/validation/test splits of any dataset - either custom or present in TensorFlow hub dataset library - using Splits API
Part: 3
Title: Exporting Your Data into the Training Pipeline
Description:This week you will extend your knowledge of data pipelines
Part: 4
Title:Performance
Description:You'll learn how to handle your data input to avoid bottlenecks, race conditions and more!
Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. In this third course, you will: - Perform streamlined ETL tasks using TensorFlow Data Services - Load different datasets and custom feature vectors using TensorFlow Hub and TensorFlow Data Services APIs - Create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset - Optimize data pipelines that become a bottleneck in the training process - Publish your own datasets to the TensorFlow Hub library and share standardized data with researchers and developers around the world This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
