From ca0f62bda7572b18bc957953ead685a682a7df5c Mon Sep 17 00:00:00 2001 From: "Christine P. Chai" Date: Fri, 3 Oct 2025 21:44:03 -0700 Subject: [PATCH] DOC: Update the URL to a backpropagation LSTM article --- content/tutorial-nlp-from-scratch.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/tutorial-nlp-from-scratch.md b/content/tutorial-nlp-from-scratch.md index a4771883..67b3dce5 100644 --- a/content/tutorial-nlp-from-scratch.md +++ b/content/tutorial-nlp-from-scratch.md @@ -652,7 +652,7 @@ def initialize_grads(parameters): return grads ``` -Now, for each gate and the fully connected layer, we define a function to calculate the gradient of the loss with respect to the input passed and the parameters used. To understand the mathematics behind how the derivatives were calculated we suggest you to follow this helpful [blog](https://christinakouridi.blog/2019/06/19/backpropagation-lstm/) by Christina Kouridi. +Now, for each gate and the fully connected layer, we define a function to calculate the gradient of the loss with respect to the input passed and the parameters used. To understand the mathematics behind how the derivatives were calculated we suggest you to follow this helpful [blog](https://christinakouridi.github.io/posts/backprop-lstm/) by Christina Kouridi. Define a function to calculate the gradients in the **Forget Gate**: