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.
Ce cours fait partie de la Spécialisation Deep Learning
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À propos de ce cours
Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML
Compétences que vous acquerrez
- 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
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Programme de cours : ce que vous apprendrez dans ce cours
Practical Aspects of Deep Learning
Optimization Algorithms
Hyperparameter Tuning, Batch Normalization and Programming Frameworks
Avis
- 5 stars88,21 %
- 4 stars10,60 %
- 3 stars1 %
- 2 stars0,11 %
- 1 star0,05 %
Meilleurs avis pour IMPROVING DEEP NEURAL NETWORKS: HYPERPARAMETER TUNING, REGULARIZATION AND OPTIMIZATION
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.
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.
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.
Excellent course. Bit tougher than first course. For those who have done Machine Learning course earlier and wondered that first course feels almost similar, second course is the 'real' next course.
À propos du Spécialisation Deep Learning

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