From 2b921273c2c3927e035d87c6ce5346df83631fbd Mon Sep 17 00:00:00 2001 From: Saksham Dhull Date: Wed, 5 Nov 2025 21:54:21 +0530 Subject: [PATCH] Fix grammar and formatting in learn.md Corrected minor grammatical issues and formatting in the learn.md file. --- .../learn.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/questions/137_implement-a-dense-block-with-2d-convolutions/learn.md b/questions/137_implement-a-dense-block-with-2d-convolutions/learn.md index f0239863..6c39decf 100644 --- a/questions/137_implement-a-dense-block-with-2d-convolutions/learn.md +++ b/questions/137_implement-a-dense-block-with-2d-convolutions/learn.md @@ -4,7 +4,7 @@ Dense blocks are a key innovation in the DenseNet architecture. Each layer recei ### Dense Block Concept For a dense block: -- **Each layer**: Applies ReLU, then 2D convolution, and then concatenates the output to previous features. +- **Each layer**: Applies ReLU, then 2D convolution, and then concatenates the output to the previous features. - Mathematically: $$ x_l = H_l([x_0, x_1, \ldots, x_{l-1}]) @@ -28,10 +28,10 @@ $$ - After $L$ layers, total channels = input channels + $L \times \text{growth rate}$. ### Putting It All Together -1️⃣ Start with an input tensor. -2️⃣ Repeat for $\text{num layers}$: +1. Start with an input tensor. +2. Repeat for $\text{num layers}$: - Apply ReLU activation. - Apply 2D convolution (with padding). - Concatenate the output along the channel dimension. -By understanding these core principles, you’re ready to build the dense block function! +By understanding these core principles, you're ready to build the dense block function!