About this Course
4.8
10,858개의 평가
1,261개의 리뷰
전문분야

다음의 5/5개 강좌

100% 온라인

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유연한 마감

유연한 마감

일정에 따라 마감일을 재설정합니다.
중급 단계

중급 단계

Hours to complete

완료하는 데 약 18시간 필요

권장: 11 hours/week...
사용 가능한 언어

영어

자막: 영어, 중국어 (간체자)

귀하가 습득할 기술

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

다음의 5/5개 강좌

100% 온라인

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유연한 마감

유연한 마감

일정에 따라 마감일을 재설정합니다.
중급 단계

중급 단계

Hours to complete

완료하는 데 약 18시간 필요

권장: 11 hours/week...
사용 가능한 언어

영어

자막: 영어, 중국어 (간체자)

강의 계획 - 이 강좌에서 배울 내용

1
Hours to complete
완료하는 데 6시간 필요

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 videos (Total 112 min), 4 quizzes
Video12개의 동영상
Notation9m
Recurrent Neural Network Model16m
Backpropagation through time6m
Different types of RNNs9m
Language model and sequence generation12m
Sampling novel sequences8m
Vanishing gradients with RNNs6m
Gated Recurrent Unit (GRU)17m
Long Short Term Memory (LSTM)9m
Bidirectional RNN8m
Deep RNNs5m
Quiz1개 연습문제
Recurrent Neural Networks20m
2
Hours to complete
완료하는 데 4시간 필요

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 videos (Total 102 min), 3 quizzes
Video10개의 동영상
Using word embeddings9m
Properties of word embeddings11m
Embedding matrix5m
Learning word embeddings10m
Word2Vec12m
Negative Sampling11m
GloVe word vectors11m
Sentiment Classification7m
Debiasing word embeddings11m
Quiz1개 연습문제
Natural Language Processing & Word Embeddings20m
3
Hours to complete
완료하는 데 5시간 필요

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 videos (Total 103 min), 3 quizzes
Video11개의 동영상
Picking the most likely sentence8m
Beam Search11m
Refinements to Beam Search11m
Error analysis in beam search9m
Bleu Score (optional)16m
Attention Model Intuition9m
Attention Model12m
Speech recognition8m
Trigger Word Detection5m
Conclusion and thank you2m
Quiz1개 연습문제
Sequence models & Attention mechanism20m
4.8
1,261개의 리뷰Chevron Right
진로

39%

이 강좌를 수료한 후 새로운 경력 시작하기
경력 혜택

39%

이 강좌를 통해 확실한 경력상 이점 얻기
경력 프로모션

11%

급여 인상 또는 승진하기

최상위 리뷰

대학: JYOct 30th 2018

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.

대학: 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!

강사

Avatar

Andrew Ng

CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
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Head Teaching Assistant - Kian Katanforoosh

Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
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Teaching Assistant - Younes Bensouda Mourri

Mathematical & Computational Sciences, Stanford University, deeplearning.ai

deeplearning.ai 정보

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

심층 학습 전문 분야 정보

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....
심층 학습

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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