Update ch07.ipynb

ch07/01_main-chapter-code/ch07.ipynb

@@ -2757,7 +2757,7 @@
     "  1. [Direct Preference Optimization (DPO) for LLM Alignment (From Scratch)](../04_preference-tuning-with-dpo/dpo-from-scratch.ipynb) implements a popular preference tuning mechanism to align the model from this chapter more closely with human preferences\n",
     "  2. [Llama 3.2 From Scratch (A Standalone Notebook)](../../ch05/07_gpt_to_llama/standalone-llama32.ipynb), a from-scratch implementation of Meta AI's popular Llama 3.2, including loading the official pretrained weights; if you are up to some additional experiments, you can replace the `GPTModel` model in each of the chapters with the `Llama3Model` class (it should work as a 1:1 replacement)\n",
     "  3. [Converting GPT to Llama](../../ch05/07_gpt_to_llama) contains code with step-by-step guides that explain the differences between GPT-2 and the various Llama models\n",
-    "  4. [Understanding the Difference Between Embedding Layers and Linear Layers](../../ch03/03_bonus_embedding-vs-matmul/embeddings-and-linear-layers.ipynb) is a conceptual explanation illustrating that the `Embedding` layer in PyTorch, which we use at the input stage of an LLM, is mathematically equivalent to a linear layer applied to one-hot encoded data\n",
+    "  4. [Understanding the Difference Between Embedding Layers and Linear Layers](../../ch02/03_bonus_embedding-vs-matmul/embeddings-and-linear-layers.ipynb) is a conceptual explanation illustrating that the `Embedding` layer in PyTorch, which we use at the input stage of an LLM, is mathematically equivalent to a linear layer applied to one-hot encoded data\n",
     "- Happy further reading!"
    ]
   }