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@amaarora really explained #convolutions very well in #fastbook week 12 session which can be viewed here
I wasn't able to write a blog post explaining my learnings from the stream but would threfore write a 🧵
I wasn't able to write a blog post explaining my learnings from the stream but would threfore write a 🧵
2/n
After going through 1st part of #convolutions chapter, have cleared a concept and was introduced to two new concepts.
1. How depthwise convolutions work (3/n)
2. Dilated convolutions (7/n)
3. Alternate interpretation of #stride (9/n)
After going through 1st part of #convolutions chapter, have cleared a concept and was introduced to two new concepts.
1. How depthwise convolutions work (3/n)
2. Dilated convolutions (7/n)
3. Alternate interpretation of #stride (9/n)
3/n
When we have a n-channel input and a m-channel output, we need to convolve over not only 2-Dimensions (W x H) but also across the depth D.
An RGB image for example has 3 channels
Let us consider we want to derive 10 feature maps from this input.
When we have a n-channel input and a m-channel output, we need to convolve over not only 2-Dimensions (W x H) but also across the depth D.
An RGB image for example has 3 channels
Let us consider we want to derive 10 feature maps from this input.
Today I’ve come across some wonderful @fastdotai YouTube channels with some excellent content! Below is a thread of my findings for folks to check out (I’ve subscribed to them all!) 1/
First and foremost we have the wonderful work coming out of @ai_fast_track. Along with the #IceVision videos he’s also done quite a number of videos exploring the @fastdotai API with some EXCELLENT videos, I’m certainly taking notes youtube.com/channel/UCht9j…
Next we have some videos by @philwhln. His first two short videos on #fastbook show some great insights into dealing with issues he had, and a great overview of the first few chapters 2/ youtube.com/user/philwhln
