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There are 2 modules in this course
In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.
By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning.
This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience.
The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.
Streamline and optimize your ML production workflow by implementing strategic guidelines for goal-setting and applying human-level performance to help define key priorities.
What's included
13 videos3 readings1 assignment
Show info about module content
13 videos•Total 100 minutes
Why ML Strategy•3 minutes
Orthogonalization•11 minutes
Single Number Evaluation Metric•7 minutes
Satisficing and Optimizing Metric•6 minutes
Train/Dev/Test Distributions•7 minutes
Size of the Dev and Test Sets•6 minutes
When to Change Dev/Test Sets and Metrics?•11 minutes
Why Human-level Performance?•6 minutes
Avoidable Bias•7 minutes
Understanding Human-level Performance•11 minutes
Surpassing Human-level Performance•6 minutes
Improving your Model Performance•5 minutes
Andrej Karpathy Interview•15 minutes
3 readings•Total 5 minutes
Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas!•2 minutes
Lecture Notes W1•1 minute
Machine Learning Flight Simulator (Introduction to the Quizzes)•2 minutes
1 assignment•Total 75 minutes
Bird Recognition in the City of Peacetopia (Quiz Case Study) •75 minutes
ML Strategy
Week 2•4 hours to complete
Module details
Develop time-saving error analysis procedures to evaluate the most worthwhile options to pursue and gain intuition for how to split your data and when to use multi-task, transfer, and end-to-end deep learning.
What's included
11 videos2 readings1 assignment
Show info about module content
11 videos•Total 132 minutes
Carrying Out Error Analysis•11 minutes
Cleaning Up Incorrectly Labeled Data•13 minutes
Build your First System Quickly, then Iterate•5 minutes
Training and Testing on Different Distributions•11 minutes
Bias and Variance with Mismatched Data Distributions•18 minutes
Addressing Data Mismatch•10 minutes
Transfer Learning•11 minutes
Multi-task Learning•13 minutes
What is End-to-end Deep Learning?•12 minutes
Whether to use End-to-end Deep Learning•10 minutes
Ruslan Salakhutdinov Interview•17 minutes
2 readings•Total 11 minutes
Lecture Notes W2•1 minute
Acknowledgments•10 minutes
1 assignment•Total 75 minutes
Autonomous Driving (Quiz Case Study) •75 minutes
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Learner reviews
4.8
50,153 reviews
5 stars
82.93%
4 stars
13.61%
3 stars
2.80%
2 stars
0.48%
1 star
0.15%
Showing 3 of 50153
P
PD
5·
Reviewed on Feb 24, 2020
Generally, the course is great. This is a short course and could be combined with other courses in this series. Also, some knowledge such as data splitting has been introduced in the courses before.
W
WG
5·
Reviewed on Mar 18, 2019
Though it might not seem imminently useful, the course notes I've referred back to the most come from this class. This course is could be summarized as a machine learning master giving useful advice.
N
NC
5·
Reviewed on May 10, 2020
Really a good course and got an insight into how to structure a machine learning project and some useful techniques for deep learning, such as transfer learning, multi-task, and end-to-end learning
When will I have access to the lectures and assignments?
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. 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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.