This course is part of the Deep Learning Specialization

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Andrew Ng +2 more instructors

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Deep Learning SpecializationDeepLearning.AI

About this Course

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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.

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Shareable Certificate

Earn a Certificate upon completion

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Includes hands on learning projects.

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Course 2 of 5 in the

Deep Learning Specialization

Intermediate Level

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

Approx. 23 hours to complete

English

Skills you will gain

Tensorflow
Deep Learning
Mathematical Optimization
hyperparameter tuning

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 2 of 5 in the

Deep Learning Specialization

Intermediate Level

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

Approx. 23 hours to complete

English

Instructors

Instructor rating

4.92/5 (4,571 Ratings)

Offered by

DeepLearning.AI

Syllabus - What you will learn from this course

Content Rating96%(67,441 ratings)

Week1

Week 1

12 hours to complete

Practical Aspects of Deep Learning

12 hours to complete

15 videos (Total 131 min), 4 readings, 6 quizzes

15 videos

Train / Dev / Test sets12mBias / Variance8mBasic Recipe for Machine Learning6mRegularization9mWhy Regularization Reduces Overfitting?7mDropout Regularization9mUnderstanding Dropout7mOther Regularization Methods8mNormalizing Inputs5mVanishing / Exploding Gradients6mWeight Initialization for Deep Networks6mNumerical Approximation of Gradients6mGradient Checking6mGradient Checking Implementation Notes5mYoshua Bengio Interview25m

4 readings

Clarification about Upcoming Regularization Video1mClarification about Upcoming Understanding Dropout Video1mHow to Download your NotebookHow to Refresh your Workspace5m

1 practice exercise

Practical aspects of Deep Learning 50m

Week2

Week 2

5 hours to complete

Optimization Algorithms

5 hours to complete

11 videos (Total 92 min), 2 readings, 3 quizzes

11 videos

Mini-batch Gradient Descent11mUnderstanding Mini-batch Gradient Descent11mExponentially Weighted Averages5mUnderstanding Exponentially Weighted Averages9mBias Correction in Exponentially Weighted Averages4mGradient Descent with Momentum9mRMSprop7mAdam Optimization Algorithm7mLearning Rate Decay6mThe Problem of Local Optima5mYuanqing Lin Interview13m

2 readings

Clarification about Upcoming Adam Optimization Video1mClarification about Learning Rate Decay Video1m

1 practice exercise

Optimization Algorithms50m

Week3

Week 3

6 hours to complete

Hyperparameter Tuning, Batch Normalization and Programming Frameworks

6 hours to complete

11 videos (Total 103 min), 5 readings, 3 quizzes

11 videos

Tuning Process7mUsing an Appropriate Scale to pick Hyperparameters8mHyperparameters Tuning in Practice: Pandas vs. Caviar6mNormalizing Activations in a Network8mFitting Batch Norm into a Neural Network12mWhy does Batch Norm work?11mBatch Norm at Test Time5mSoftmax Regression11mTraining a Softmax Classifier10mDeep Learning Frameworks4mTensorFlow15m

5 readings

Clarification about Upcoming Normalizing Activations in a Network Video1mClarifications about Upcoming Softmax Video1m(Optional) Learn about Gradient Tape and More1mReferences10mAcknowledgments10m

1 practice exercise

Hyperparameter tuning, Batch Normalization, Programming Frameworks50m

Reviews

4.9

7191 reviews

5 stars
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4 stars
10.60%
3 stars
1%
2 stars
0.11%
1 star
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TOP REVIEWS FROM IMPROVING DEEP NEURAL NETWORKS: HYPERPARAMETER TUNING, REGULARIZATION AND OPTIMIZATION

by KCDec 19, 2019

Excellent content. The grader seriously needs to be updated thogh. For example, it needs to be Python2 and Tensorflow2 compatible and also needs to be robust in handling common syntaxes such as "-=".

by CMDec 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 ABAug 26, 2021

Amazing course which focus on the theoretical part of parameters tuning, but it needs more explanation of Tensorflow, as I felt a little lost in the last project. Except that, it is an amazing course.

by YLFeb 20, 2018

very useful course, especially the last tensorflow assignment. the only reason i gave 4 stars is due to the lack of practice on batchnorm, which i believe is one of the most usefule techniques lately.

View all reviews

About the Deep Learning Specialization

The Deep Learning Specialization is a 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 and train 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. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also 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. 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.

More questions? Visit the Learner Help Center.

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

About this Course

Skills you will gain

Instructors

Andrew Ng
Top Instructor

Kian Katanforoosh
Top Instructor

Younes Bensouda Mourri
Top Instructor

Offered by

DeepLearning.AI

Syllabus - What you will learn from this course

Practical Aspects of Deep Learning

Optimization Algorithms

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

Skills you will gain

Instructors

Andrew NgTop Instructor

Kian KatanforooshTop Instructor

Younes Bensouda MourriTop Instructor

Offered by

DeepLearning.AI

Syllabus - What you will learn from this course

Practical Aspects of Deep Learning

Optimization Algorithms

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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Learn Something New

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Andrew Ng
Top Instructor

Kian Katanforoosh
Top Instructor

Younes Bensouda Mourri
Top Instructor