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.
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课程信息
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学生职业成果
39%
完成这些课程后已开始新的职业生涯
40%
通过此课程获得实实在在的工作福利
13%
加薪或升职
您将获得的技能
Recurrent Neural NetworkArtificial Neural NetworkDeep LearningLong Short-Term Memory (ISTM)
学生职业成果
39%
完成这些课程后已开始新的职业生涯
40%
通过此课程获得实实在在的工作福利
13%
加薪或升职
100% 在线
立即开始，按照自己的计划学习。
第 5 门课程（共 5 门）
可灵活调整截止日期
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中级
完成时间大约为18 小时
建议：12 hours/week
英语（English）
字幕：中文（繁体）, 中文（简体）, 韩语, 英语（English）, 西班牙语（Spanish）
教学大纲 - 您将从这门课程中学到什么
完成时间为 6 小时
Recurrent Neural Networks
Learn about recurrent neural networks. This type of model has been proven to perform extremely well on temporal data. It has several variants including LSTMs, GRUs and Bidirectional RNNs, which you are going to learn about in this section.
完成时间为 6 小时
12 个视频 （总计 112 分钟）, 2 个阅读材料, 4 个测验
12 个视频
Notation9分钟
Recurrent Neural Network Model16分钟
Backpropagation through time6分钟
Different types of RNNs9分钟
Language model and sequence generation12分钟
Sampling novel sequences8分钟
Vanishing gradients with RNNs6分钟
Gated Recurrent Unit (GRU)17分钟
Long Short Term Memory (LSTM)9分钟
Bidirectional RNN8分钟
Deep RNNs5分钟
2 个阅读材料
Gated Recurrent Unit (GRU) *CORRECTION*1分钟
Long Short Term Memory (LSTM) *CORRECTION*1分钟
1 个练习
Recurrent Neural Networks30分钟
完成时间为 4 小时
Natural Language Processing & Word Embeddings
Natural language processing with deep learning is an important combination. Using word vector representations and embedding layers you can train recurrent neural networks with outstanding performances in a wide variety of industries. Examples of applications are sentiment analysis, named entity recognition and machine translation.
完成时间为 4 小时
10 个视频 （总计 102 分钟）, 1 个阅读材料, 3 个测验
10 个视频
Using word embeddings9分钟
Properties of word embeddings11分钟
Embedding matrix5分钟
Learning word embeddings10分钟
Word2Vec12分钟
Negative Sampling11分钟
GloVe word vectors11分钟
Sentiment Classification7分钟
Debiasing word embeddings11分钟
1 个阅读材料
GloVe word vectors *CORRECTION*1分钟
1 个练习
Natural Language Processing & Word Embeddings30分钟
完成时间为 5 小时
Sequence models & Attention mechanism
Sequence models can be augmented using an attention mechanism. This algorithm will help your model understand where it should focus its attention given a sequence of inputs. This week, you will also learn about speech recognition and how to deal with audio data.
完成时间为 5 小时
11 个视频 （总计 103 分钟）, 3 个阅读材料, 3 个测验
11 个视频
Basic Models6分钟
Picking the most likely sentence8分钟
Beam Search11分钟
Refinements to Beam Search11分钟
Error analysis in beam search9分钟
Bleu Score (optional)16分钟
Attention Model Intuition9分钟
Attention Model12分钟
Speech recognition8分钟
Trigger Word Detection5分钟
Conclusion and thank you2分钟
3 个阅读材料
Bleu Score *CORRECTION*1分钟
Corrections10分钟
Instructions if you are unable to open your notebook10分钟
1 个练习
Sequence models & Attention mechanism30分钟
审阅
4.8
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 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 JR 提供May 25, 2019
I am so grateful that Andrew and the team provided such good course, I learn so much from this course, I am so excited that see the wake word detection model actually work in the programming exercise
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 SB 提供Feb 18, 2018
Loved the course - it was very interesting. It is also pretty complex, so will probably go through it again to review the concepts and how the models work. Thank you for this wonderful course series!
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 MI 提供Oct 15, 2019
This is one of the most comprehensive yet enjoyable courses in the whole specialization! There are several assignments of practical applications. Thanks for the time and effort put into this course.
由 AA 提供Mar 3, 2018
Dr. Ng and team did a great job! Dr. Ng delivered even the most complicated concepts in the most lucid way possible. Assignments created by the team are awesome and very good to work on! 5/5 course!
由 GS 提供Apr 26, 2019
So many possibilities will be presented in front of you after this course. The only limit is the boundary of my imagination and creativity, that is how I feel now upon the completion of this course.
由 CH 提供Feb 14, 2020
One of the best thing from this class is not only we can understand the concept of RNN, LSTM, etc, but also I also get the idea about how these technique can be used in many daily life applications
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deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
关于 深度学习 专项课程
If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.
You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.
AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.
We will help you master Deep Learning, understand how to apply it, and build a career in AI.
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