About this Course
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다음 전문 분야의 3개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 18시간 필요


자막: 중국어 (번체자), 아랍어, 프랑스어, 우크라이나어, 중국어 (간체자), 포르투갈어 (브라질), 한국어, 터키어, 영어, 스페인어, 일본어...

귀하가 습득할 기술

Artificial Neural NetworkBackpropagationPython ProgrammingDeep Learning

다음 전문 분야의 3개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 18시간 필요


자막: 중국어 (번체자), 아랍어, 프랑스어, 우크라이나어, 중국어 (간체자), 포르투갈어 (브라질), 한국어, 터키어, 영어, 스페인어, 일본어...

Course을(를) 수강하는 학습자

  • Data Scientists
  • Machine Learning Engineers
  • Biostatisticians
  • Researchers
  • Data Engineers

강의 계획 - 이 강좌에서 배울 내용

완료하는 데 2시간 필요

Introduction to deep learning

7개 동영상 (총 76분), 2 readings, 1 quiz
7개의 동영상
What is a neural network?7m
Supervised Learning with Neural Networks8m
Why is Deep Learning taking off?10m
About this Course2m
Course Resources1m
Geoffrey Hinton interview40m
2개의 읽기 자료
Frequently Asked Questions10m
How to use Discussion Forums10m
1개 연습문제
Introduction to deep learning20m
완료하는 데 8시간 필요

Neural Networks Basics

19개 동영상 (총 161분), 7 readings, 3 quizzes
19개의 동영상
Logistic Regression5m
Logistic Regression Cost Function8m
Gradient Descent11m
More Derivative Examples10m
Computation graph3m
Derivatives with a Computation Graph14m
Logistic Regression Gradient Descent6m
Gradient Descent on m Examples8m
More Vectorization Examples6m
Vectorizing Logistic Regression7m
Vectorizing Logistic Regression's Gradient Output9m
Broadcasting in Python11m
A note on python/numpy vectors6m
Quick tour of Jupyter/iPython Notebooks3m
Explanation of logistic regression cost function (optional)7m
Pieter Abbeel interview16m
7개의 읽기 자료
Clarification about Upcoming Logistic Regression Cost Function Video1m
Clarification about Upcoming Gradient Descent Video1m
Derivation of DL/dz part 110m
Derivation of DL/dz part 210m
Clarification of "dz"10m
Deep Learning Honor Code2m
Programming Assignment FAQ10m
1개 연습문제
Neural Network Basics20m
완료하는 데 5시간 필요

Shallow neural networks

12개 동영상 (총 109분), 2 readings, 2 quizzes
12개의 동영상
Neural Network Representation5m
Computing a Neural Network's Output9m
Vectorizing across multiple examples9m
Explanation for Vectorized Implementation7m
Activation functions10m
Why do you need non-linear activation functions?5m
Derivatives of activation functions7m
Gradient descent for Neural Networks9m
Backpropagation intuition (optional)15m
Random Initialization7m
Ian Goodfellow interview14m
2개의 읽기 자료
Clarification: Activation Function1m
Clarification about Upcoming Backpropagation intuition (optional)1m
1개 연습문제
Shallow Neural Networks20m
완료하는 데 5시간 필요

Deep Neural Networks

8개 동영상 (총 64분), 3 readings, 3 quizzes
8개의 동영상
Forward Propagation in a Deep Network7m
Getting your matrix dimensions right11m
Why deep representations?10m
Building blocks of deep neural networks8m
Forward and Backward Propagation10m
Parameters vs Hyperparameters7m
What does this have to do with the brain?3m
3개의 읽기 자료
Clarification about Getting your matrix dimensions right video1m
Clarification about Upcoming Forward and Backward Propagation Video1m
Clarification about What does this have to do with the brain video1m
1개 연습문제
Key concepts on Deep Neural Networks20m
11468개의 리뷰Chevron Right


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신경망 및 딥 러닝의 최상위 리뷰

대학: AASep 2nd 2019

I highly appreciated the interviews at the end of some weeks. I am currently trying to transition from a research background in Systems/Computational Biology to work professionally in deep learning :)

대학: JPFeb 12th 2018

I would love some pointers to additional references for each video. Also, the instructor keeps saying that the math behind backprop is hard. What about an optional video with that? Otherwise, awesome!



Andrew Ng

CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of 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
Computer Science

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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