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.
제공자:


이 강좌에 대하여
At the rate of 5 hours a week, it typically takes 4 weeks to complete this course.
귀하가 습득할 기술
- Deep Learning
- Inductive Transfer
- Machine Learning
- Multi-Task Learning
- Decision-Making
At the rate of 5 hours a week, it typically takes 4 weeks to complete this course.
제공자:

deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
강의 계획표 - 이 강좌에서 배울 내용
ML Strategy
Streamline and optimize your ML production workflow by implementing strategic guidelines for goal-setting and applying human-level performance to help define key priorities.
ML Strategy
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.
검토
- 5 stars82.81%
- 4 stars13.77%
- 3 stars2.80%
- 2 stars0.47%
- 1 star0.12%
STRUCTURING MACHINE LEARNING PROJECTS의 최상위 리뷰
It's a great course! This course gave me a lot of new perspectives in constructing a machine learning project. Especially, the discussion of data distribution in the train/dev/test set is fantastic.
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)
Very helpful tips for navigating possible problems that would likely occur while building/training a model. The "pilot-training" exercieses, that mimick real-life problems / projects, are excellent !
I learned so many things in this module. I learned that how to do error analysis and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.
심층 학습 특화 과정 정보
The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.

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