Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

This course is part of the Deep Learning Specialization

4.9
stars
57,297 ratings

Andrew Ng +2 more instructors
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Deep Learning SpecializationDeepLearning.AI

About this Course

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically. 
By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow.

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.

Learner Career Outcomes

30%

started a new career after completing these courses

29%

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 2 of 5 in the

Deep Learning Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML

Approx. 18 hours to complete

English

Subtitles: Chinese (Traditional), Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Portuguese (Brazilian), Vietnamese, Korean, German, Russian, Turkish, English, Spanish

Skills you will gain

TensorflowDeep LearningMathematical Optimizationhyperparameter tuning

Learner Career Outcomes

30%

started a new career after completing these courses

29%

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 2 of 5 in the

Deep Learning Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML

Approx. 18 hours to complete

English

Subtitles: Chinese (Traditional), Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Portuguese (Brazilian), Vietnamese, Korean, German, Russian, Turkish, English, Spanish

Instructors

Instructor rating

4.92/5 (3,221 Ratings)

Offered by

DeepLearning.AI

DeepLearning.AI is an education technology company that develops a global community of AI talent.

Syllabus - What you will learn from this course

Content Rating96%(58,130 ratings)

Week

Week 1

8 hours to complete

Practical aspects of Deep Learning

8 hours to complete

15 videos (Total 131 min), 3 readings, 4 quizzes

15 videos

Train / Dev / Test sets12m

Bias / Variance8m

Basic Recipe for Machine Learning6m

Regularization9m

Why regularization reduces overfitting?7m

Dropout Regularization9m

Understanding Dropout7m

Other regularization methods8m

Normalizing inputs5m

Vanishing / Exploding gradients6m

Weight Initialization for Deep Networks6m

Numerical approximation of gradients6m

Gradient checking6m

Gradient Checking Implementation Notes5m

Yoshua Bengio interview25m

3 readings

Clarification about Upcoming Regularization Video1m

Clarification about Upcoming Understanding dropout Video1m

Clarification about Upcoming Normalizing Inputs Video1m

1 practice exercise

Practical aspects of deep learning30m

Week

Week 2

5 hours to complete

Optimization algorithms

5 hours to complete

11 videos (Total 92 min), 2 readings, 2 quizzes

11 videos

Mini-batch gradient descent11m

Understanding mini-batch gradient descent11m

Exponentially weighted averages5m

Understanding exponentially weighted averages9m

Bias correction in exponentially weighted averages4m

Gradient descent with momentum9m

RMSprop7m

Adam optimization algorithm7m

Learning rate decay6m

The problem of local optima5m

Yuanqing Lin interview13m

2 readings

Clarification about Upcoming Adam Optimization Video1m

Clarification about Learning Rate Decay Video1m

1 practice exercise

Optimization algorithms30m

Week

Week 3

5 hours to complete

Hyperparameter tuning, Batch Normalization and Programming Frameworks

5 hours to complete

11 videos (Total 104 min), 2 readings, 2 quizzes

11 videos

Tuning process7m

Using an appropriate scale to pick hyperparameters8m

Hyperparameters tuning in practice: Pandas vs. Caviar6m

Normalizing activations in a network8m

Fitting Batch Norm into a neural network12m

Why does Batch Norm work?11m

Batch Norm at test time5m

Softmax Regression11m

Training a softmax classifier10m

Deep learning frameworks4m

TensorFlow16m

2 readings

Clarifications about Upcoming Softmax Video1m

Note about TensorFlow 1 and TensorFlow 210m

1 practice exercise

Hyperparameter tuning, Batch Normalization, Programming Frameworks30m

Reviews

4.9

6579 reviews

5 stars
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4 stars
10.54%
3 stars
0.96%
2 stars
0.10%
1 star
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TOP REVIEWS FROM IMPROVING DEEP NEURAL NETWORKS: HYPERPARAMETER TUNING, REGULARIZATION AND OPTIMIZATION

by ASApr 18, 2020

Very good course to give you deep insight about how to enhance your algorithm and neural network and improve its accuracy. Also teaches you Tensorflow. Highly recommend especially after the 1st course

by CVDec 23, 2017

Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow Thanks.

by AMOct 8, 2019

I really enjoyed this course. Many details are given here that are crucial to gain experience and tips on things that looks easy at first sight but are important for a faster ML project implementation

by XGOct 30, 2017

Thank you Andrew!! I know start to use Tensorflow, however, this tool is not well for a research goal. Maybe, pytorch could be considered in the future!! And let us know how to use pytorch in Windows.

View all reviews

About the Deep Learning Specialization

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.

In this Specialization, you will build neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. You will master these theoretical concepts and their industry applications using Python and TensorFlow. You will tackle real-world case studies such as autonomous driving, sign language reading, music generation, computer vision, speech recognition, and natural language processing. AI is transforming many industries. The Deep Learning Specialization 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. Along the way, you will get career advice from deep learning experts from industry and academia.

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.
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.
Will I earn university credit for completing the Course?
This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

More questions? Visit the Learner Help Center.

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

About this Course

Learner Career Outcomes

30%

29%

11%

Skills you will gain

Learner Career Outcomes

30%

29%

11%

Instructors

Andrew Ng
Top Instructor

Kian Katanforoosh
Top Instructor

Teaching Assistant - Younes Bensouda Mourri
Top Instructor

Offered by

DeepLearning.AI

Syllabus - What you will learn from this course

Week 1

Practical aspects of Deep Learning

Week 2

Optimization algorithms

Week 3

Hyperparameter tuning, Batch Normalization and Programming Frameworks

Reviews

TOP REVIEWS FROM IMPROVING DEEP NEURAL NETWORKS: HYPERPARAMETER TUNING, REGULARIZATION AND OPTIMIZATION

About the Deep Learning Specialization

Frequently Asked Questions

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Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

About this Course

Learner Career Outcomes

30%

29%

11%

Skills you will gain

Learner Career Outcomes

30%

29%

11%

Instructors

Andrew NgTop Instructor

Kian KatanforooshTop Instructor

Teaching Assistant - Younes Bensouda MourriTop Instructor

Offered by

DeepLearning.AI

Syllabus - What you will learn from this course

Week 1

Practical aspects of Deep Learning

Week 2

Optimization algorithms

Week 3

Hyperparameter tuning, Batch Normalization and Programming Frameworks

Reviews

TOP REVIEWS FROM IMPROVING DEEP NEURAL NETWORKS: HYPERPARAMETER TUNING, REGULARIZATION AND OPTIMIZATION

About the Deep Learning Specialization

Frequently Asked Questions

Coursera Footer

Top Online Courses

Top Online Specializations

Online Certificates

Online Degree Programs

Coursera

Community

More

Andrew Ng
Top Instructor

Kian Katanforoosh
Top Instructor

Teaching Assistant - Younes Bensouda Mourri
Top Instructor