About this Course
4.9
26,415 ratings
2,971 reviews
Specialization

Course 2 of 5 in the

100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Beginner Level

Beginner Level

Hours to complete

Approx. 14 hours to complete

Suggested: 3 weeks, 3-6 hours per week...
Available languages

English

Subtitles: English, Chinese (Traditional), Chinese (Simplified), Korean, Turkish

Skills you will gain

HyperparameterTensorflowHyperparameter OptimizationDeep Learning
Specialization

Course 2 of 5 in the

100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Beginner Level

Beginner Level

Hours to complete

Approx. 14 hours to complete

Suggested: 3 weeks, 3-6 hours per week...
Available languages

English

Subtitles: English, Chinese (Traditional), Chinese (Simplified), Korean, Turkish

Syllabus - What you will learn from this course

Week
1
Hours to complete
8 hours to complete

Practical aspects of Deep Learning

...
Reading
15 videos (Total 131 min), 4 quizzes
Video15 videos
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
Quiz1 practice exercise
Practical aspects of deep learning20m
Week
2
Hours to complete
4 hours to complete

Optimization algorithms

...
Reading
11 videos (Total 92 min), 2 quizzes
Video11 videos
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
Quiz1 practice exercise
Optimization algorithms20m
Week
3
Hours to complete
5 hours to complete

Hyperparameter tuning, Batch Normalization and Programming Frameworks

...
Reading
11 videos (Total 104 min), 2 quizzes
Video11 videos
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
Quiz1 practice exercise
Hyperparameter tuning, Batch Normalization, Programming Frameworks20m
4.9
2,971 ReviewsChevron Right
Career direction

36%

started a new career after completing these courses
Career Benefit

32%

got a tangible career benefit from this course

Top Reviews

By CVDec 24th 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\n\nThanks.

By PGOct 31st 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.

Instructors

Avatar

Andrew Ng

CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
Avatar

Head Teaching Assistant - Kian Katanforoosh

Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
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Teaching Assistant - Younes Bensouda Mourri

Mathematical & Computational Sciences, Stanford University, deeplearning.ai

About deeplearning.ai

deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders....

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....
Deep Learning

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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.

More questions? Visit the Learner Help Center.