Sequence Models

开始时间: 04/22/2022 持续时间: Unknown

所在平台: CourseraArchive

课程类别: 计算机科学

大学或机构: CourseraNew

课程主页: https://www.coursera.org/archive/nlp-sequence-models

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课程详情

This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. - Be able to apply sequence models to natural language problems, including text synthesis. - Be able to apply sequence models to audio applications, including speech recognition and music synthesis. This is the fifth and final course of the Deep Learning Specialization. deeplearning.ai is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content.

序列模型:本课程将教您如何为自然语言,音频和其他序列数据建立模型。得益于深度学习,序列算法的性能远远优于两年前,这使语音识别,音乐合成,聊天机器人,机器翻译,自然语言理解等众多激动人心的应用成为可能。 你会: -了解如何构建和训练递归神经网络(RNN)以及常用变量,例如GRU和LSTM。 -能够将序列模型应用于自然语言问题,包括文本合成。 -能够将序列模型应用于音频应用,包括语音识别和音乐合成。 这是深度学习专业课程的第五门也是最后一门课程。 deeplearning.ai还与NVIDIA深度学习研究院(DLI)合作在课程5,序列模型中提供有关使用深度学习进行机器翻译的编程任务。您将有机会构建具有前沿,与行业相关的内容的深度学习项目。

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课程简介

This course will teach you how to build models for natural language, audio, and other sequence data.

课程标签

序列模型 RNN 递归神经网络 深度学习 Andrew_Ng 深度学习公开课 深度学习课程

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