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+# Index
+
+$g()$ is any ***Non-Linear Function***
+
+## Basic Architecture
+
+### Multiplicative Modules
+
+With these modules we can modify our ***traditional*** ways of ***neural networks***
+and implement ***switch-like*** functions
+
+#### Professor's one
+
+Basically here we want a ***way to modify `weights` with `inputs`***.
+
+Here $\vec{z}$ and $\vec{x}$ are both `inputs`
+
+$$
+\begin{aligned}
+    \vec{y} &= \sum_{w_{i,j}x{j}} \\
+    \vec{w} &= \sum_{k} u_{i,j,k} z_{k} \rightarrow \\
+    \rightarrow \vec{y} &= \sum_{j,k} u_{i,j,k} z_{k} x_{j}
+\end{aligned}
+$$
+
+As we can see here, $z_{k}$ modifies, along $u$, $x_{j}$.
+
+#### Quadratic Layer
+
+This layer expands data by applying the **quadratic formula**
+
+$$
+\begin{aligned}
+    \vec{v} &= [a_1, a_2, a_3] \\
+
+    quad\_layer(\vec{v}) &= [ a_1 \cdot a_1, a_1 \cdot a_2, a_1 \cdot a_3, ... , a_3 \cdot a_3 ]
+\end{aligned}
+$$
+
+#### Product Unit[^product-unit]
+
+$$
+o_k =  \sum_{j}^{m} v_{k,j} \cdot \left( \prod_{i=1}^{n} x_{i}^{w_{j,i}}\right) + v_{k,0}
+$$
+
+#### Sigma-Pi Unit[^simga-pi][^simga-pi-2]
+
+This *layer* is basically a product of `input` terms **times**
+a `weight`, intead of a `matrix multiplication` of a `linear-layer`.
+
+Moreover, this is ***not necessarily*** `fully-connected`
+
+$$
+o_k = g\left( \sum_{q \in conjunct} w_{q} \prod_{k=1}^{N} z_{q,k} \right)
+$$
+
+### Attention Modules
+
+They define a way for our `model` to get what's ***more important***
+
+#### Softmax
+
+We use this function to output the ***importance*** of a certain
+value over all the others.
+
+$$
+\begin{aligned}
+    \sigma(\vec{x})_{j} &= \frac{e^{x_{j}}}{\sum_{k} e^{x_{k}}} \;\; \forall k \in {0, ..., N} \\
+
+    \sigma(\vec{x})_{j} &\in [0, 1] \;\; \forall j \in {0, ..., N}
+\end{aligned}
+$$
+
+## Mixture of Experts[^mixture-of-experts]
+
+What happens if we have more `models` and we want to take their output?
+
+Basically we have a set of `weights` over our `outputs` before the `output-layer`.
+Both the **experts** and the **gating-function** need to be `trained`.
+
+> [!TIP]
+>
+> Since we are talking about `weights` and `importance`, probably here it is better to use an [attention-model](#attention-modules)
+
+## Parameter Transformation
+
+It is basically when the `wheights` are the `output` of a ***function***
+
+Since they are controlled by some other `parameters`, then we need to ***learn***
+those instead
+
+### Weights Sharing
+
+Here we ***copy*** our weights over more ***basic components***.
+Since we have ***more than one value*** for our `original weights`, then we need to ***sum*** those.
+
+> [!TIP]
+>
+> This is used to find ***motifs*** on an `input`
+
+<!-- Footnotes -->
+
+[^simga-pi]: [University of Pretoria | sigma-pi | pg. 2](https://repository.up.ac.za/bitstream/handle/2263/29715/03chapter3.pdf?sequence=4#:~:text=A%20pi%2Dsigma%20network%20\(PSN,of%20sums%20of%20input%20components.)
+[^simga-pi-2]:[]
+[^product-unit]: doi: 10.13053/CyS-20-2-2218
+[^mixture-of-experts]: [Wikipedia | 1st April 2025](https://en.wikipedia.org/wiki/Mixture_of_experts)