feat: add new slides on LLM training paradigms and further reading resources

content/slides/ch01-what-is-ai.tsx

@@ -394,6 +394,13 @@ export const ch01: Chapter = {
             detection head — which is why it converges fast on the small
             datasets we collect at competitions.
           </p>
+          <Callout label="Further reading" tone="accent">
+            <a className="underline" href="https://huggingface.co/blog/samuellimabraz/peft-methods" target="_blank" rel="noreferrer">
+              PEFT: Parameter-Efficient Fine-Tuning Methods
+            </a>
+            {" — "}LoRA, adapters, prefix-tuning and friends: how to fine-tune
+            a foundation model with &lt; 1 % of its parameters.
+          </Callout>
         </div>
       ),
       viz: <FoundationModels />,

content/slides/ch03-paradigms.tsx

@@ -195,6 +195,56 @@ export const ch03: Chapter = {
       ),
       viz: <AgentEnvLoop />,
     },
+    {
+      id: "ch03-llm",
+      title: "Modern LLMs use all three",
+      eyebrow: "Pretrain · fine-tune · align",
+      layout: "split",
+      content: (
+        <div className="space-y-4">
+          <p>
+            The training pipeline behind GPT-4, LLaMA, and Gemini composes the
+            paradigms we just saw, in this order:
+          </p>
+          <ol className="space-y-2 text-[14px] text-ink/85">
+            <li>
+              <strong>1. Self-supervised pretraining</strong> — predict the
+              next token on trillions of tokens of unlabelled text. Produces a
+              general-purpose base model.
+            </li>
+            <li>
+              <strong>2. Supervised fine-tuning (SFT)</strong> — train on a
+              much smaller set of high-quality instruction / answer pairs.
+              Teaches the format.
+            </li>
+            <li>
+              <strong>3. RLHF</strong> — reinforcement learning from human
+              feedback. A reward model learns from human preferences; the
+              policy is then optimised against it (PPO or DPO).
+            </li>
+          </ol>
+          <p className="text-muted">
+            Yann LeCun&apos;s 2016 &quot;cake&quot; analogy survives in this
+            form: most of the calories come from unsupervised pretraining;
+            SFT and RLHF are the icing and the cherry.
+          </p>
+        </div>
+      ),
+      viz: (
+        <figure className="mx-auto flex w-full max-w-[760px] flex-col items-center">
+          <div className="w-full overflow-hidden rounded-md border border-stroke bg-surface">
+            <img
+              src="/figures/llm-paradigms.jpg"
+              alt="Unsupervised pretraining, supervised fine-tuning, and RLHF, drawn as a monstrous creature with a cherry on top."
+              className="block w-full"
+            />
+          </div>
+          <figcaption className="mt-3 text-center font-mono text-[11px] uppercase tracking-[0.12em] text-muted">
+            modern reimagining of LeCun&apos;s &quot;cake&quot; analogy
+          </figcaption>
+        </figure>
+      ),
+    },
     {
       id: "ch03-05",
       title: "Where each fits in Black Bee",

content/slides/ch05-deep-learning.tsx

@@ -244,6 +244,15 @@ export const ch05: Chapter = {
             differentiate it automatically — autograd. We only have to write
             the forward pass.
           </p>
+          <Callout label="Implemented from scratch">
+            <a className="underline" href="https://github.com/samuellimabraz/cafedl" target="_blank" rel="noreferrer">
+              cafedl
+            </a>{" "}
+            — a small deep-learning library I wrote in Java to internalise
+            backprop, layer composition, and OOP design patterns. Useful if
+            you want to see every line of an autograd engine before reaching
+            for PyTorch.
+          </Callout>
         </div>
       ),
       viz: <BackpropChain />,

content/slides/ch11-nectar.tsx

@@ -246,6 +246,16 @@ ObjectDetectionEvaluator(detector.model, cfg).evaluate()`}
               </tbody>
             </table>
           </div>
+          <Callout label="Worked example, published" tone="accent">
+            For a full, public end-to-end detection project that follows the
+            same pipeline (data → architecture comparison → training →
+            evaluation → deployment), see{" "}
+            <a className="underline" href="https://huggingface.co/blog/samuellimabraz/signature-detection-model" target="_blank" rel="noreferrer">
+              Open-Source Handwritten Signature Detection Model
+            </a>{" "}
+            — YOLO vs DETR vs RF-DETR with real numbers and an open model on
+            the Hub.
+          </Callout>
         </div>
       ),
     },

content/slides/ch12-closing.tsx

@@ -36,6 +36,37 @@ export const ch12: Chapter = {
               <li>· COCO mAP definition (cocodataset.org)</li>
             </ul>
           </div>
+          <div className="md:col-span-2">
+            <div className="mb-3 font-mono text-[11px] uppercase tracking-[0.12em] text-muted">
+              Worked examples and side projects
+            </div>
+            <ul className="space-y-2 text-[15px]">
+              <li>
+                ·{" "}
+                <a className="underline" href="https://huggingface.co/blog/samuellimabraz/signature-detection-model" target="_blank" rel="noreferrer">
+                  Signature detection blog
+                </a>{" "}
+                — full detection pipeline, YOLO vs DETR vs RF-DETR, with
+                published model and dataset.
+              </li>
+              <li>
+                ·{" "}
+                <a className="underline" href="https://huggingface.co/blog/samuellimabraz/peft-methods" target="_blank" rel="noreferrer">
+                  PEFT methods
+                </a>{" "}
+                — LoRA, adapters, prefix-tuning: fine-tuning foundation models
+                cheaply.
+              </li>
+              <li>
+                ·{" "}
+                <a className="underline" href="https://github.com/samuellimabraz/cafedl" target="_blank" rel="noreferrer">
+                  cafedl
+                </a>{" "}
+                — Java deep-learning library written from scratch; an autograd
+                engine you can read end-to-end.
+              </li>
+            </ul>
+          </div>
         </div>
       ),
     },