What is Optimization? + Learning Gradient Descent | Two Minute Papers #82 | Summary and Q&A

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July 29, 2016
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What is Optimization? + Learning Gradient Descent | Two Minute Papers #82

TL;DR

Mathematical optimization is a technique used to find the optimal solution to a problem by adjusting variables and minimizing or maximizing an objective function.

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Questions & Answers

Q: What is mathematical optimization?

Mathematical optimization is a technique that involves adjusting variables to find the best possible solution to a problem by minimizing or maximizing an objective function.

Q: How is optimization used in different fields?

Optimization is widely used in various fields like computer science, engineering, and deep learning to solve complex problems and improve efficiency.

Q: What is gradient descent?

Gradient descent is a simple optimization algorithm that involves adjusting variables and finding the direction that leads to the most favorable changes in the objective function.

Q: Can optimization algorithms be learned?

Yes, the DeepMind paper shows that optimization algorithms can emerge as a result of learning and can outperform previously existing methods on specialized problems.

Summary & Key Takeaways

  • Mathematical optimization involves finding the best possible solution by adjusting variables and optimizing an objective function.

  • Optimization is used in various fields like computer science, engineering, and deep learning.

  • Gradient descent is a popular optimization algorithm that involves making small changes to variables to find the most favorable results.

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