Consider the following paragraph from NUMERICAL COMPUTATION of the deep learning book..
Suppose we have a function $y = f(x)$, where both $x$ and $y$ are real numbers. The derivative of this function is denoted as $f'(x)$ or as $\dfrac{dy}{dx}$. The derivative $f'(x)$ gives the slope of $f(x)$ at the point $x$. In other words, it specifies how to scale a small change in the input to obtain the corresponding change in the output: $f(x+\epsilon) \approx f(x) + \epsilon f'(x)$.
I have doubt in the equation $f(x+\epsilon) \approx f(x) + \epsilon f'(x)$ given in the paragraph.
In strict sense, the derivative function $f'$ of a real valued function $f$ is defined as
$$f'(x) = \lim_{\epsilon \rightarrow 0} \dfrac{f(x+\epsilon)-f(x)}{\epsilon}$$
wherever the limit exists.
If I replace the original definition of the derivative as follows
$$f'(x) \approx \dfrac{f(x+\epsilon)-f(x)}{\epsilon}$$
then I can obtain the equation given in the paragraph i.e, $f(x+\epsilon) \approx f(x) + \epsilon f'(x)$.
But, my doubt is that how can I modify the definition with $\lim\limits_{\epsilon \rightarrow 0}$ to an approximation with out limit? How can the following two are same?
$$f'(x) = \lim_{\epsilon \rightarrow 0} \dfrac{f(x+\epsilon)-f(x)}{\epsilon} \text { and } f'(x) \approx \dfrac{f(x+\epsilon)-f(x)}{\epsilon}$$