About this Course

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Learner Career Outcomes

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started a new career after completing these courses

37%

got a tangible career benefit from this course

12%

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Shareable Certificate
Earn a Certificate upon completion
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Start instantly and learn at your own schedule.
Course 2 of 5 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level
Approx. 18 hours to complete
English

Skills you will gain

HyperparameterTensorflowHyperparameter OptimizationDeep Learning

Learner Career Outcomes

41%

started a new career after completing these courses

37%

got a tangible career benefit from this course

12%

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
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level
Approx. 18 hours to complete
English

Offered by

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DeepLearning.AI

Syllabus - What you will learn from this course

Content RatingThumbs Up96%(54,383 ratings)Info
Week
1

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
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
2

Week 2

5 hours to complete

Optimization algorithms

5 hours to complete
11 videos (Total 92 min), 2 readings, 2 quizzes
11 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
2 readings
Clarification about Upcoming Adam Optimization Video1m
Clarification about Learning Rate Decay Video1m
1 practice exercise
Optimization algorithms30m
Week
3

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
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

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About the Deep Learning Specialization

Deep Learning

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