Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
What is about?
This paper introduced a successful method for initializing Neural Networks which use non-linear functions such as Relu, called here rectifiers.
As the title says, the performance made it possible to surpass human-level performance on Imagenet.
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
What is about?
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
What is about?
This paper introduced a successful method for initializing Neural Networks which use non-linear functions such as Relu, called here rectifiers.
As the title says, the performance made it possible to surpass human-level performance on Imagenet.
| status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
|---|---|---|---|---|---|---|---|
| repetition number in this series | 0 | memorised on | scheduled repetition | ||||
| scheduled repetition interval | last repetition or drill |