Frechet derivative is a generalization of total derivative to the normed linear spaces. They have analogous definition. For example, the Frechet/total differential $df(x,h)$ of a functional/function $f:D\rightarrow Y$ at $x$ with increment $h$ is a bounded linear operator such that
$$
\lim_{\lVert h\rVert\rightarrow 0}\frac{\lVert f(x+h)-f(x)-df(x,h)\rVert}{\lVert h\rVert}=0
$$
If $f$ is defined on an open set in $\mathbb{R}^n$ with $\lVert \cdot\rVert$ being the Euclidean norm, then $df(x,h)$ is called the total differential.
If $f$ is defined on an open set in a norm linear space $(X,\lVert \cdot\rVert)$ with some arbitrary norm $\lVert \cdot\rVert$, then $df(x,h)$ is called the Frechet differential.
A differential is a function of the increment $h$. In both cases, we may have the representation
$$
df(x,h)=A(x)h.
$$
In $\mathbb{R}^n$, $A(x)$ maps $x$ to the space of $m\times n$ matrices called the Jacobians, $m$ is the dimension of $Y$. In arbitrary normed linear space $(X,\lVert\cdot\rVert_X)$ and $(Y,\lVert \cdot\rVert_Y)$. $A(x)$ maps $x$ to the space of bounded linear operators between $X$ and $Y$. Therefore, the mapping $A(x)$ is called total/Frechet derivative accordingly.
Since $(\mathbb{R}^n,\lVert \cdot\rVert_2)$ is a norm linear space, total derivative is always Frechet derivative, which corresponds to the case you mentioned.
For motivation, it turns out a lot of the Classical calculus results can be extended to Banach spaces (e.g. space of infinite sequences, space of integrable functions, etc.). It is really powerful for solving Calculus of Variantion problems such as finding the time path (continuous function) of a particle that minimizes energies or finding the optimal investment sequence over a long horizon for an economy.
