Sequences, Time Series and Prediction

所在平台: CourseraArchive

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大学或机构: CourseraNew

课程主页: https://www.coursera.org/archive/tensorflow-sequences-time-series-and-prediction

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课程大纲

Sequences and Prediction
Deep Neural Network for time series
Recurrent Neural Networks for time series
Real-world time series data

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If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In this fourth course, you will learn how to build time series models in TensorFlow. You’ll first implement best practices to prepare time series data. You’ll also explore how RNNs and 1D ConvNets can be used for prediction. Finally, you’ll apply everything you’ve learned throughout the Specialization to build a sunspot prediction model using real-world data! The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

序列,时间序列和预测:如果您是想要构建可扩展的AI驱动算法的软件开发人员,则需要了解如何使用这些工具来构建它们。本专业将教您使用TensorFlow的最佳实践,TensorFlow是一种流行的机器学习开源框架。 在这第四门课程中,您将学习如何在TensorFlow中构建时间序列模型。首先,您将采用最佳做法来准备时间序列数据。您还将探索如何将RNN和1D ConvNet用于预测。最后,您将运用在本专业学习中学到的所有知识,使用实际数据构建黑子预测模型! Ng的机器学习课程和深度学习专业知识教授了机器学习和深度学习的最重要和最基本的原理。这个新的deeplearning.ai TensorFlow专业化课程教您如何使用TensorFlow实施这些原理,以便您可以开始构建可伸缩模型并将其应用于实际问题。为了更深入地了解神经网络的工作原理,建议您参加“深度学习专业化”课程。

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