... - Deepstash

...

Max Pooling Layers : These layers reduce the feature map (the size of the features), reduce the computational resources needed and prevents overfitting

Fully Connected Layers : They appear at the end of the CNN, they’re just linear layers which takes the results of the CNN as inputs and outputs the lable

That’s the architecture, but the really amazing part of a CNN are it’s kernels inside it’s convolutional layers! These kernels are only matrixes (basically a bunch of numbers sorted in a box), but they’re able to extract tons of features about the image.

31

232 reads

CURATED FROM

IDEAS CURATED BY

vedarham

 卐 || एकं सत विप्रा बहुधा वदन्ति || Enthusiast || Collection Of Some Best Reads || Decentralizing...

The idea is part of this collection:

The Art of Leadership

Learn more about artificialintelligence with this collection

How to build trust and respect with team members

How to communicate effectively

How to motivate and inspire others

Related collections

Read & Learn

20x Faster

without
deepstash

with
deepstash

with

deepstash

Personalized microlearning

100+ Learning Journeys

Access to 200,000+ ideas

Access to the mobile app

Unlimited idea saving

Unlimited history

Unlimited listening to ideas

Downloading & offline access

Supercharge your mind with one idea per day

Enter your email and spend 1 minute every day to learn something new.

Email

I agree to receive email updates