If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago.
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About this Course
최근 조회 2,142,107회
학습자 경력 결과
39%
이 강좌를 수료한 후 새로운 경력 시작하기
38%
이 강좌를 통해 확실한 경력상 이점 얻기
11%
급여 인상 또는 승진하기
귀하가 습득할 기술
Artificial Neural NetworkBackpropagationPython ProgrammingDeep Learning
학습자 경력 결과
39%
이 강좌를 수료한 후 새로운 경력 시작하기
38%
이 강좌를 통해 확실한 경력상 이점 얻기
11%
급여 인상 또는 승진하기
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 전문 분야의 5개 강좌 중 1번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 18시간 필요
영어
자막: 중국어 (번체자), 아랍어, 프랑스어, 우크라이나어, 중국어 (간체자), 포르투갈어 (브라질), 베트남어, 한국어, 터키어, 영어, 스페인어, 일본어
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 2시간 필요
Introduction to deep learning
Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today.
완료하는 데 2시간 필요
7개 동영상 (총 76분), 2 readings, 1 quiz
7개의 동영상
Welcome5m
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 learning30m
완료하는 데 8시간 필요
Neural Networks Basics
Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models.
완료하는 데 8시간 필요
19개 동영상 (총 161분), 6 readings, 3 quizzes
19개의 동영상
Logistic Regression5m
Logistic Regression Cost Function8m
Gradient Descent11m
Derivatives7m
More Derivative Examples10m
Computation graph3m
Derivatives with a Computation Graph14m
Logistic Regression Gradient Descent6m
Gradient Descent on m Examples8m
Vectorization8m
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
6개의 읽기 자료
Clarification about Upcoming Logistic Regression Cost Function Video1m
Clarification about Upcoming Gradient Descent Video1m
Derivation of DL/dz (optional reading)10m
Clarification of "dz"10m
Deep Learning Honor Code2m
Programming Assignment FAQ10m
1개 연습문제
Neural Network Basics30m
완료하는 데 5시간 필요
Shallow neural networks
Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.
완료하는 데 5시간 필요
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 Networks30m
완료하는 데 5시간 필요
Deep Neural Networks
Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.
완료하는 데 5시간 필요
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 Networks30m
검토
4.9
신경망 및 딥 러닝의 최상위 리뷰
NT 제공Jan 17, 2020
Very structured approach to developing a neural network which I believe I can use as foundation for any project regardless its complexity. Thanks professor Andrew Ng and the team for their dedication.
WY 제공Jun 25, 2018
Really, really good course. Especially the tips of avoiding possible bugs due to shapes. Also impressed by the heroes' stories. Genuinely inspired and thoughtfully educated by Professor Ng. Thank you!
XL 제공Aug 26, 2017
This is a very good course for people who want to get started with neural networks. Andrew did a great job explaining the math behind the scenes. Assignments are well-designed too. Highly recommended.
AS 제공Oct 7, 2017
Its a great course, but I wish things like multiclass classification and regression were also included, also I think there should be more emphasis on different cost functions and their properties etc.
SK 제공Aug 29, 2018
Nothing can get better than this course from Professor Andrew Ng. A must for every Data science enthusiast. Gets you up to speed right from the fundamentals. Thanks a lot for Prof Andrew and his team.
MC 제공Apr 21, 2019
This is a great course. The teaching quality and the standard of course materials clearly reflect the effort the instructors put in. As a result, I have increased my understanding of NN significantly.
SG 제공Jul 14, 2019
Dear Andrew! Thank you so very much for making me belive in myself as a machine learning engineer. Your lectures & excercises are like "shoulders of Giants" on which a good student can stand out high.
AG 제공Mar 6, 2019
I understand all those thing which you have discussed in this course and I also like the way first tell story of concet and assign assignment. Now I fall in love with neural network and deep learning.
MH 제공Jun 29, 2018
Very good course to start Deep learning. But you need to have the basic idea first. I would suggest to do the Stanford Andrew Ng Machine Learning course first and then take this specialization courses
GC 제공May 30, 2019
I have learnt a lot of tricks with numpy and I believe I have a better understanding of what a NN does. Now it does not look like a black box anymore. I look forward to see what's in the next courses!
LV 제공Apr 6, 2019
A bit easy (python wise) but maybe that's just a reflection of personal experience / practice. The contest is easy to digest (week to week) and the intuitions are well thought of in their explanation.
SS 제공Nov 26, 2017
Fantastic introduction to deep NNs starting from the shallow case of logistic regression and generalizing across multiple layers. The material is very well structured and Dr. Ng is an amazing teacher.
제공자:
deeplearning.ai
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.
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