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.
This course is part of the Deep Learning Specialization
Offered By
Deep Learning Specializationdeeplearning.aiAbout this Course
4,275,644 recent views
Learner Career Outcomes
39%
started a new career after completing these courses
38%
got a tangible career benefit from this course
11%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 1 of 5 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 20 hours to complete
English
Subtitles: Chinese (Traditional), Arabic, French, Ukrainian, Chinese (Simplified), Portuguese (Brazilian), Vietnamese, Korean, Turkish, English, Spanish, Japanese
Skills you will gain
Artificial Neural NetworkBackpropagationPython ProgrammingDeep Learning
Learner Career Outcomes
39%
started a new career after completing these courses
38%
got a tangible career benefit from this course
11%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 1 of 5 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 20 hours to complete
English
Subtitles: Chinese (Traditional), Arabic, French, Ukrainian, Chinese (Simplified), Portuguese (Brazilian), Vietnamese, Korean, Turkish, English, Spanish, Japanese
Offered by
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.
Syllabus - What you will learn from this course
2 hours to complete
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.
2 hours to complete
7 videos (Total 76 min), 2 readings, 1 quiz
7 videos
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
2 readings
Frequently Asked Questions10m
How to use Discussion Forums10m
1 practice exercise
Introduction to deep learning30m
8 hours to complete
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.
8 hours to complete
19 videos (Total 161 min), 7 readings, 3 quizzes
19 videos
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
7 readings
Clarification about Upcoming Logistic Regression Cost Function Video1m
Clarification about Upcoming Gradient Descent Video1m
Derivation of DL/dz (optional reading)10m
Copy of Clarification about Upcoming Logistic Regression Cost Function Video1m
Clarification of "dz"10m
Deep Learning Honor Code2m
Programming Assignment FAQ10m
1 practice exercise
Neural Network Basics30m
5 hours to complete
Shallow neural networks
Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.
5 hours to complete
12 videos (Total 109 min), 2 readings, 2 quizzes
12 videos
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
2 readings
Clarification: Activation Function1m
Clarification about Upcoming Backpropagation intuition (optional)1m
1 practice exercise
Shallow Neural Networks30m
5 hours to complete
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.
5 hours to complete
8 videos (Total 64 min), 3 readings, 3 quizzes
8 videos
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
3 readings
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
1 practice exercise
Key concepts on Deep Neural Networks30m
Reviews
TOP REVIEWS FROM NEURAL NETWORKS AND DEEP LEARNING
by NTJan 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.
by JNFeb 19, 2018
I know this is intended for a broad audience, but I found that the assignments were too easy. Not that they are testing easy material, but that the answers are almost stated directly in the questions.
by AGMar 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.
by ATJun 29, 2020
I think that this course went a little bit too much into needy greedy details of the math behind deep neural networks, but overall I think that it is a great place to start a journey in deep learning!
About the Deep Learning Specialization
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.
Frequently Asked Questions
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.
More questions? Visit the Learner Help Center.
