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.
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
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Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
This course is part of Deep Learning Specialization
Instructors: Andrew Ng
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Gain insight into a topic and learn the fundamentals.
4.9
(63,413 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 23 hours
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Assessments
3 assignments
Taught in English
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This course is part of the Deep Learning Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course
Discover and experiment with a variety of different initialization methods, apply L2 regularization and dropout to avoid model overfitting, then apply gradient checking to identify errors in a fraud detection model.
What's included
15 videos5 readings1 assignment3 programming assignments
Develop your deep learning toolbox by adding more advanced optimizations, random minibatching, and learning rate decay scheduling to speed up your models.
What's included
11 videos3 readings1 assignment1 programming assignment
Explore TensorFlow, a deep learning framework that allows you to build neural networks quickly and easily, then train a neural network on a TensorFlow dataset.
What's included
11 videos7 readings1 assignment1 programming assignment
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4.9
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Showing 3 of 63413
RR
4
Reviewed on Jun 12, 2020
Could have increased assignments and some more indepth knowledge of tensorflow and proper installation way of tensorflow cause mine is showing error when iam trying to practice as shown in the video
AA
4
Reviewed on Oct 22, 2017
Assignment in week 2 could not tell the difference between 'a-=b' and 'a=a-b' and marked the former as incorrect even though they are the same and gave the same output. Other than that, a great course
MD
4
Reviewed on May 5, 2018
Would have liked to see the math and more complete explanations for all the things that Prof. Ng glosses over by saying "you don't really need to understand XYZ". Even if this material was optional.
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