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.
提供方
深度学习 專項課程deeplearning.ai課程信息
1,050,314 次近期查看
學生職業成果
39%
完成這些課程後已開始新的職業生涯
40%
通過此課程獲得實實在在的工作福利
13%
加薪或升職
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 5 門課程（共 5 門）
可靈活調整截止日期
根據您的日程表重置截止日期。
中級
完成時間大約為16 小時
英語（English）
字幕：中文（繁體）, 法語（French）, 巴西葡萄牙語, 中文（簡體）, 韓語, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
您將獲得的技能
Recurrent Neural NetworkArtificial Neural NetworkDeep LearningLong Short-Term Memory (ISTM)
學生職業成果
39%
完成這些課程後已開始新的職業生涯
40%
通過此課程獲得實實在在的工作福利
13%
加薪或升職
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 5 門課程（共 5 門）
可靈活調整截止日期
根據您的日程表重置截止日期。
中級
完成時間大約為16 小時
英語（English）
字幕：中文（繁體）, 法語（French）, 巴西葡萄牙語, 中文（簡體）, 韓語, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
提供方
deeplearning.ai
Founded by Andrew Ng, deeplearning.ai is on a mission to make world-class AI education accessible to people around the globe so that we can all benefit from an AI-powered future.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 6 小時
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.
完成時間為 6 小時
12 個視頻 （總計 112 分鐘）, 2 個閱讀材料, 4 個測驗
12 個視頻
Notation9分鐘
Recurrent Neural Network Model16分鐘
Backpropagation through time6分鐘
Different types of RNNs9分鐘
Language model and sequence generation12分鐘
Sampling novel sequences8分鐘
Vanishing gradients with RNNs6分鐘
Gated Recurrent Unit (GRU)17分鐘
Long Short Term Memory (LSTM)9分鐘
Bidirectional RNN8分鐘
Deep RNNs5分鐘
2 個閱讀材料
Gated Recurrent Unit (GRU) *CORRECTION*1分鐘
Long Short Term Memory (LSTM) *CORRECTION*1分鐘
1 個練習
Recurrent Neural Networks30分鐘
完成時間為 4 小時
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.
完成時間為 4 小時
10 個視頻 （總計 102 分鐘）, 1 個閱讀材料, 3 個測驗
10 個視頻
Using word embeddings9分鐘
Properties of word embeddings11分鐘
Embedding matrix5分鐘
Learning word embeddings10分鐘
Word2Vec12分鐘
Negative Sampling11分鐘
GloVe word vectors11分鐘
Sentiment Classification7分鐘
Debiasing word embeddings11分鐘
1 個閱讀材料
GloVe word vectors *CORRECTION*1分鐘
1 個練習
Natural Language Processing & Word Embeddings30分鐘
完成時間為 5 小時
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.
完成時間為 5 小時
11 個視頻 （總計 103 分鐘）, 4 個閱讀材料, 3 個測驗
11 個視頻
Basic Models6分鐘
Picking the most likely sentence8分鐘
Beam Search11分鐘
Refinements to Beam Search11分鐘
Error analysis in beam search9分鐘
Bleu Score (optional)16分鐘
Attention Model Intuition9分鐘
Attention Model12分鐘
Speech recognition8分鐘
Trigger Word Detection5分鐘
Conclusion and thank you2分鐘
4 個閱讀材料
Bleu Score *CORRECTION*1分鐘
Corrections10分鐘
Workera's Standardized Tests for AI Skills1分鐘
Instructions if you are unable to open your notebook10分鐘
1 個練習
Sequence models & Attention mechanism30分鐘
審閱
來自SEQUENCE MODELS的熱門評論
由 PJ 提供2019年4月3日
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2018年3月13日
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!
由 JY 提供2018年10月29日
The lectures covers lots of SOTA deep learning algorithms and the lectures are well-designed and easy to understand. The programming assignment is really good to enhance the understanding of lectures.
由 AM 提供2019年6月30日
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.
關於 深度学习 專項課程
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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