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.
This course is part of the Deep Learning Specialization
Offered By


About this Course
Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML
Skills you will gain
- Tensorflow
- Deep Learning
- Mathematical Optimization
- hyperparameter tuning
Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML
Offered by
Syllabus - What you will learn from this course
Practical Aspects of Deep Learning
Optimization Algorithms
Hyperparameter Tuning, Batch Normalization and Programming Frameworks
Reviews
- 5 stars88.21%
- 4 stars10.60%
- 3 stars1%
- 2 stars0.11%
- 1 star0.05%
TOP REVIEWS FROM IMPROVING DEEP NEURAL NETWORKS: HYPERPARAMETER TUNING, REGULARIZATION AND OPTIMIZATION
Everything, Everyparameter in neural networks looks familiar to me now. I feel like I can optimize them for better accuracy. Overall I learned some new things and the way of teaching was really nice.
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.
Fantastic course! For the first time, I now have a better intuition for optimizing and tuning hyperparameters used for deep neural networks.I got motivated to learn more after completing this course.
After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.
About the Deep Learning Specialization

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