このコースについて
4.8
8,984 ratings
1,050 reviews
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....
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次の専門講座における5コース5

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100%オンラインコース

自分のスケジュールですぐに学習を始めてください。
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柔軟性のある期限

スケジュールに従って期限をリセットします。
Intermediate Level

中級レベル

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推奨:9 hours/week

約16時間で修了
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English

字幕:English, Chinese (Simplified)

習得するスキル

Recurrent Neural NetworkArtificial Neural NetworkDeep LearningLong Short-Term Memory (ISTM)
Stacks

次の専門講座における5コース5

Globe

100%オンラインコース

自分のスケジュールですぐに学習を始めてください。
Calendar

柔軟性のある期限

スケジュールに従って期限をリセットします。
Intermediate Level

中級レベル

Clock

推奨:9 hours/week

約16時間で修了
Comment Dots

English

字幕:English, Chinese (Simplified)

シラバス - 本コースの学習内容

1

セクション
Clock
6時間で修了

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....
Reading
12本の動画(合計112分), 4 quizzes
Video12件のビデオ
Notation9 分
Recurrent Neural Network Model16 分
Backpropagation through time6 分
Different types of RNNs9 分
Language model and sequence generation12 分
Sampling novel sequences8 分
Vanishing gradients with RNNs6 分
Gated Recurrent Unit (GRU)17 分
Long Short Term Memory (LSTM)9 分
Bidirectional RNN8 分
Deep RNNs5 分
Quiz1の練習問題
Recurrent Neural Networks20 分

2

セクション
Clock
4時間で修了

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....
Reading
10本の動画(合計102分), 3 quizzes
Video10件のビデオ
Using word embeddings9 分
Properties of word embeddings11 分
Embedding matrix5 分
Learning word embeddings10 分
Word2Vec12 分
Negative Sampling11 分
GloVe word vectors11 分
Sentiment Classification7 分
Debiasing word embeddings11 分
Quiz1の練習問題
Natural Language Processing & Word Embeddings20 分

3

セクション
Clock
5時間で修了

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....
Reading
11本の動画(合計103分), 3 quizzes
Video11件のビデオ
Picking the most likely sentence8 分
Beam Search11 分
Refinements to Beam Search11 分
Error analysis in beam search9 分
Bleu Score (optional)16 分
Attention Model Intuition9 分
Attention Model12 分
Speech recognition8 分
Trigger Word Detection5 分
Conclusion and thank you2 分
Quiz1の練習問題
Sequence models & Attention mechanism20 分
4.8
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人気のレビュー

by WKMar 14th 2018

I was really happy because I could learn deep learning from Andrew Ng.\n\nThe lectures were fantastic and amazing.\n\nI was able to catch really important concepts of sequence models.\n\nThanks a lot!

by SBFeb 19th 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!

講師

Andrew Ng

Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain

Head Teaching Assistant - Kian Katanforoosh

Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec

Teaching Assistant - Younes Bensouda Mourri

Mathematical & Computational Sciences, Stanford University, deeplearning.ai

deeplearning.aiについて

deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders....

Deep Learningの専門講座について

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....
Deep Learning

よくある質問

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。