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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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42 小时
英语（English）
字幕：英语（English）, 韩语, 阿拉伯语（Arabic）
您将获得的技能
- Natural Language Processing
- Long Short Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
- Recurrent Neural Network
- Attention Models
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42 小时
英语（English）
字幕：英语（English）, 韩语, 阿拉伯语（Arabic）
提供方
deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
授课大纲 - 您将从这门课程中学到什么
完成时间为 14 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 14 小时
12 个视频 （总计 112 分钟）, 4 个阅读材料, 6 个测验
完成时间为 9 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 9 小时
10 个视频 （总计 99 分钟）, 1 个阅读材料, 4 个测验
完成时间为 10 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 10 小时
10 个视频 （总计 98 分钟）, 3 个阅读材料, 4 个测验
完成时间为 9 小时
Transformer Network
完成时间为 9 小时
5 个视频 （总计 42 分钟）, 4 个阅读材料, 3 个测验
审阅
- 5 stars83.60%
- 4 stars13.07%
- 3 stars2.55%
- 2 stars0.48%
- 1 star0.28%
来自序列模型的热门评论
由 SS 提供Jan 1, 2020
Learnt a lot about new concepts in RNN and LSTM. Really wanted to learn about these models. This course helped a lot. Everything was new and so fascinating. Loved this course and our teach Andrew NG.
由 SD 提供Sep 27, 2018
Great hands on instruction on how RNNs work and how they are used to solve real problems. It was particularly useful to use Conv1D, Bidirectional and Attention layers into RNNs and see how they work.
由 NM 提供Feb 20, 2018
Hope can elaborate the backpropagation of RNN much more. BP through time is a bit tricky though we do not need to think about it during implementation using most of existing deep learning frameworks.
由 AM 提供Jul 4, 2018
Excellent course! This course extensively covers all of the relevant areas of NLP with a strong practical element allowing you to applying Deep Learning for Sequence Models in real-world scenarios.
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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