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+# GANs
+
+## Pseudorandom Numbers
+
+### Rejection Sampling
+
+Instead of sampling directly over the **complex distribution**, we sample over a **simpler one**
+and accept or reject values according to some conditions.
+
+If these conditions are crafted in a specific way, our samples will resemble those of the **complex distribution**
+
+### Metropolis-Hasting
+
+The idea is of **constructing a stationary Markov Chain** along the possible values we can sample.
+
+Then we take a long path and see where we end up and sample that point
+
+### Inverse Transform
+
+The idea is of sampling directly from a **well known distribution** and then return a number from the **complex distribution** such
+that its probability function is higher than our **simple sample**
+
+$$
+u \in \mathcal{U}[0, 1] \\
+\text{take } x \text{ so that } u \leq F(x) \text{ where } \\
+F(x) \text{ is the the cumulative distribution funcion of } x
+$$
+
+As proof we have that
+
+$$
+F_{X}(x) \in [0, 1] = X \rightarrow F_{X}^{-1}(X) = x \in \R \\
+\text{Let's define } Y = F_{X}^{-1}(U) \\
+F_{Y}(y) =
+    P(Y \leq y) =
+    P(F_{X}^{-1}(U) \leq y) \rightarrow \\
+\rightarrow P(F_{X}(F_{X}^{-1}(U)) \leq F_{X}(y)) =
+    P(U \leq F_{X}(y)) = F_{X}(y)
+$$
+
+In particular, we demonstrated that by crafting a variable such as that,
+makes it possible to sample $X$ and $Y$ by sampling $U$
+
+> [!NOTE]
+> The last passage says that a uniform variable is less than a value, that
+> comes from a distribution. Since $F_{U}(x) = P(U \leq x) = x$ the last
+> part holds.
+>
+> We can use the CDF of X in the probability function, without inverting the
+> inequality because any CDF is positively monotone
+
+## Generative Models
+
+The idea is that, given a $n \times n$ vector $N$, made of stacked pixels,
+not all vectors will be a dog photo, so we must find the probability
+distribution associated with dog images
+
+However we have little to no information about the **actual distribution**
+of dog images.
+
+Our solution is to **sample from a simple distribution, transform** it
+into our target distribution and then **compare with** a **sampled subset** of
+the **complex distribution**
+
+We then train our network to make better transformations
+
+### Direct Learning
+
+We see the MMD distance between our generated set and the real one
+
+### Indirect Learning
+
+We introduce the concept of a **discriminator** which will guess whether
+our image was generated or it comes from the actual distribution.
+
+We then update our model based on the grade achieved by the **discriminator**
+
+## Generative Adversarial Networks
+
+If we use the **indirect learning** approach, we need a `Network` capable of
+classificating generated and genuine content according to their labels.
+
+However, for the same reasons of the **generative models**, we don't have such
+a `Network` readily available, but rather we have to **learn** it.
+
+## Training Phases
+
+We can train both the **generator an discriminator** together. They will
+`backpropagate` based on **classification errors** of the **discriminator**,
+however they will have 2 distinct objective:
+
+- `Generator`: **Maximize** this error
+- `Discriminator`: **Minimize** this error
+
+From a **game theory** view, they are playing a **zero-sum** game and the
+perfect outcome is **when the discriminator matches tags 50% of times**
+
+### Loss functions
+
+Assuming that $G(\vec{z})$ is the result of the `generator`, $D(\vec{a})$ is the result of the `discriminator`, $y = 0$ is the generated content and $y = 1$ is the real one
+
+- **`Generator`**:\
+We want to maximize the error of the `Discriminator` $D(\vec{a})$ when $y = 0$
+
+$$
+\max_{G}\{ -(1 -y) \log(1 - D(G(\vec{z})))\} =
+    \max_{G}\{- \log(1 - D(G(\vec{z})))\}
+$$
+
+
+
+
+- **`Discriminator`**: \
+We want to minimize its error when $y = 1$. However, if the `Generator` is good at its job
+$D(G(\vec{z})) \approx 1$ the above equation
+will be near 0, so we use these instead:
+
+$$
+\begin{aligned}
+\min_{G}\{ (1 -y) \log(1 - D(G(\vec{z})))\} &=
+    \min_{G}\{\log(1 - D(G(\vec{z})))\} \\
+    &\approx \max_{G}\{- \log(D(G(\vec{z})))\}
+\end{aligned}
+$$
+
+However they are basically playing a `minimax` game:
+
+$$
+\min_{D} \max_{G} \{
+- E_{x \sim Data} \log(D(\vec{x})) - E_{z \sim Noise} \log(1 - D(G(\vec{z})))
+\}
+$$
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