Add "What's next" section (#432)

* Add What's next section

* Delete appendix-D/01_main-chapter-code/appendix-D-Copy2.ipynb

* Delete ch03/01_main-chapter-code/ch03-Copy1.ipynb

* Delete appendix-D/01_main-chapter-code/appendix-D-Copy1.ipynb

* Update ch07.ipynb

* Update ch07.ipynb

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

@@ -2743,6 +2743,23 @@
     "- The [./load-finetuned-model.ipynb](./load-finetuned-model.ipynb) notebook illustrates how to load the finetuned model in a new session\n",
     "- You can find the exercise solutions in [./exercise-solutions.ipynb](./exercise-solutions.ipynb)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9cc51ec-e06c-4470-b626-48401a037851",
+   "metadata": {},
+   "source": [
+    "## What's next?\n",
+    "\n",
+    "- Congrats on completing the book; in case you are looking for additional resources, I added several bonus sections to this GitHub repository that you might find interesting\n",
+    "- The complete list of bonus materials can be viewed in the main README's [Bonus Material](https://github.com/rasbt/LLMs-from-scratch?tab=readme-ov-file#bonus-material) section\n",
+    "- To highlight a few of my favorites:\n",
+    "  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](../../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!"
+   ]
   }
  ],
  "metadata": {