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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深度学习 专项课程deeplearning.ai课程信息
4,334,837 次近期查看
学生职业成果
39%
完成这些课程后已开始新的职业生涯
38%
通过此课程获得实实在在的工作福利
11%
加薪或升职
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 1 门课程（共 5 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为20 小时
英语（English）
字幕：中文（繁体）, 阿拉伯语（Arabic）, 法语（French）, 乌克兰语, 中文（简体）, 巴西葡萄牙语, 越南语, 韩语, 土耳其语（Turkish）, 英语（English）, 西班牙语（Spanish）, 日语
您将获得的技能
Artificial Neural NetworkBackpropagationPython ProgrammingDeep Learning
学生职业成果
39%
完成这些课程后已开始新的职业生涯
38%
通过此课程获得实实在在的工作福利
11%
加薪或升职
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 1 门课程（共 5 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为20 小时
英语（English）
字幕：中文（繁体）, 阿拉伯语（Arabic）, 法语（French）, 乌克兰语, 中文（简体）, 巴西葡萄牙语, 越南语, 韩语, 土耳其语（Turkish）, 英语（English）, 西班牙语（Spanish）, 日语
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.
教学大纲 - 您将从这门课程中学到什么
完成时间为 2 小时
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.
完成时间为 2 小时
7 个视频 （总计 76 分钟）, 2 个阅读材料, 1 个测验
7 个视频
Welcome5分钟
What is a neural network?7分钟
Supervised Learning with Neural Networks8分钟
Why is Deep Learning taking off?10分钟
About this Course2分钟
Course Resources1分钟
Geoffrey Hinton interview40分钟
2 个阅读材料
Frequently Asked Questions10分钟
How to use Discussion Forums10分钟
1 个练习
Introduction to deep learning30分钟
完成时间为 8 小时
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.
完成时间为 8 小时
19 个视频 （总计 161 分钟）, 6 个阅读材料, 3 个测验
19 个视频
Logistic Regression5分钟
Logistic Regression Cost Function8分钟
Gradient Descent11分钟
Derivatives7分钟
More Derivative Examples10分钟
Computation graph3分钟
Derivatives with a Computation Graph14分钟
Logistic Regression Gradient Descent6分钟
Gradient Descent on m Examples8分钟
Vectorization8分钟
More Vectorization Examples6分钟
Vectorizing Logistic Regression7分钟
Vectorizing Logistic Regression's Gradient Output9分钟
Broadcasting in Python11分钟
A note on python/numpy vectors6分钟
Quick tour of Jupyter/iPython Notebooks3分钟
Explanation of logistic regression cost function (optional)7分钟
Pieter Abbeel interview16分钟
6 个阅读材料
Clarification about Upcoming Logistic Regression Cost Function Video1分钟
Clarification about Upcoming Gradient Descent Video1分钟
Derivation of DL/dz (optional reading)10分钟
Clarification of "dz"10分钟
Deep Learning Honor Code2分钟
Programming Assignment FAQ10分钟
1 个练习
Neural Network Basics30分钟
完成时间为 5 小时
Shallow neural networks
Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.
完成时间为 5 小时
12 个视频 （总计 109 分钟）, 2 个阅读材料, 2 个测验
12 个视频
Neural Network Representation5分钟
Computing a Neural Network's Output9分钟
Vectorizing across multiple examples9分钟
Explanation for Vectorized Implementation7分钟
Activation functions10分钟
Why do you need non-linear activation functions?5分钟
Derivatives of activation functions7分钟
Gradient descent for Neural Networks9分钟
Backpropagation intuition (optional)15分钟
Random Initialization7分钟
Ian Goodfellow interview14分钟
2 个阅读材料
Clarification: Activation Function1分钟
Clarification about Upcoming Backpropagation intuition (optional)1分钟
1 个练习
Shallow Neural Networks30分钟
完成时间为 5 小时
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.
完成时间为 5 小时
8 个视频 （总计 64 分钟）, 3 个阅读材料, 3 个测验
8 个视频
Forward Propagation in a Deep Network7分钟
Getting your matrix dimensions right11分钟
Why deep representations?10分钟
Building blocks of deep neural networks8分钟
Forward and Backward Propagation10分钟
Parameters vs Hyperparameters7分钟
What does this have to do with the brain?3分钟
3 个阅读材料
Clarification about Getting your matrix dimensions right video1分钟
Clarification about Upcoming Forward and Backward Propagation Video1分钟
Clarification about What does this have to do with the brain video1分钟
1 个练习
Key concepts on Deep Neural Networks30分钟
审阅
来自神经网络与深度学习的热门评论
由 AS 提供Oct 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.
由 SG 提供Jul 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.
由 XL 提供Aug 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.
由 GC 提供May 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!
关于 深度学习 专项课程
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.
常见问题
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
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.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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