In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.



Structuring Machine Learning Projects
This course is part of Deep Learning Specialization



Instructors: Andrew Ng
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There are 2 modules in this course
Streamline and optimize your ML production workflow by implementing strategic guidelines for goal-setting and applying human-level performance to help define key priorities.
What's included
13 videos3 readings1 assignment
Develop time-saving error analysis procedures to evaluate the most worthwhile options to pursue and gain intuition for how to split your data and when to use multi-task, transfer, and end-to-end deep learning.
What's included
11 videos2 readings1 assignment
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Reviewed on Jul 26, 2018
Very important and valuable intuitions about DNN training/optimization. It's full of really practical information while implementing my own models.DNN鞚 鞁れ牅 鞝侅毄頃犽晫 氚橂摐鞁 鞚错暣頃橁碃 鞝侅毄頃挫暭 頃 鞁れ鞝 雮挫毄霌る 甑劚霅 氅嬱 旖旍姢 鞛呺媹雼!
Reviewed on Feb 20, 2019
This is the knowledge in which we will get from lots of experience only, but the andrew has shared in this course which might help us in future by saving a lot of time through this course experience
Reviewed on Jun 16, 2020
Useful to know what are the steps that should be taken after obtaining results. Tho there isn't much information regarding making machine learning projects here (ie. there isn't any hands on project)
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