In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more.
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深度学习 专项课程deeplearning.ai课程信息
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通过此课程获得实实在在的工作福利
13%
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第 5 门课程（共 5 门）
中级
- Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
- A basic grasp of linear algebra & ML
完成时间大约为35 小时
英语（English）
字幕：中文（繁体）, 阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
您将获得的技能
Natural Language ProcessingLong Short Term Memory (LSTM)Gated Recurrent Unit (GRU)Recurrent Neural NetworkAttention Models
学生职业成果
38%
完成这些课程后已开始新的职业生涯
39%
通过此课程获得实实在在的工作福利
13%
加薪或升职
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 5 门课程（共 5 门）
中级
- Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
- A basic grasp of linear algebra & ML
完成时间大约为35 小时
英语（English）
字幕：中文（繁体）, 阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
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deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
教学大纲 - 您将从这门课程中学到什么
完成时间为 12 小时
Recurrent Neural Networks
Discover recurrent neural networks, a type of model that performs extremely well on temporal data, and several of its variants, including LSTMs, GRUs and Bidirectional RNNs,
完成时间为 12 小时
12 个视频 （总计 112 分钟）, 7 个阅读材料, 4 个测验
完成时间为 8 小时
Natural Language Processing & Word Embeddings
Natural language processing with deep learning is a powerful combination. Using word vector representations and embedding layers, train recurrent neural networks with outstanding performance across a wide variety of applications, including sentiment analysis, named entity recognition and neural machine translation.
完成时间为 8 小时
10 个视频 （总计 102 分钟）, 2 个阅读材料, 3 个测验
完成时间为 9 小时
Sequence Models & Attention Mechanism
Augment your sequence models using an attention mechanism, an algorithm that helps your model decide where to focus its attention given a sequence of inputs. Then, explore speech recognition and how to deal with audio data.
完成时间为 9 小时
10 个视频 （总计 100 分钟）, 4 个阅读材料, 3 个测验
完成时间为 7 小时
Transformer Network
完成时间为 7 小时
5 个视频 （总计 41 分钟）, 5 个阅读材料, 2 个测验
审阅
来自SEQUENCE MODELS的热门评论
由 AC 提供Jun 23, 2019
Very well produced and explained. In my case, the nature of the Sequence Model makes understanding the concepts and finishing the assignment more challenging than other segments of the specialization.
由 AM 提供Jun 30, 2019
The course is very good and has taught me the all the important concepts required to build a sequence model. The assignments are also very neatly and precisely designed for the real world application.
由 PJ 提供Apr 3, 2019
The previous courses raised the bar and expectations. The assignments for Week 1 and Week 2 were a bit unclear. Lectures for Week 1 and Week 2 can be improved as well. Besides, this is a great course!
由 WK 提供Mar 13, 2018
I was really happy because I could learn deep learning from Andrew Ng. The lectures were fantastic and amazing.\n\nI was able to catch really important concepts of sequence models.\n\nThanks a lot!
关于 深度学习 专项课程
The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.
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