hello everyone welcome to the semicolon
in this tutorial we're going to learn
about activation functions so according
to Wikipedia activation functions are
something which mapped a particular
output to a particular set of inputs so
they are used for containing the output
in between zero to one or any given
values they are also used to impart a
non-linearity and there's one of the
important factors which affect your
results and accuracy of your model so it
is very important that we learn about it
so these are some famous activation
functions identity activation function
binary step activation function
logistical sigmoid which we have seen in
the last tutorial Ratan hatch activation
functions are 10 Valu licky raloo and
soft max so we'll be looking at what
each of this does so identity function
is as simple as this if you have X as
your input it gives you X there's
nothing big so this is the graph of it
and whatever your curve is you will get
the exact same curve now binary step
function if your input is greater than
zero it gives you 1 and if your input is
less than 0 it gives you 0 so it takes
all the positive input makes it 1 and
all the negative inputs and make it 0 so
this is the binary step function it is
very useful in classifiers when you want
to classify it between 1 and 0 then this
is very useful when we have a logistic
or sigmoid and what this does is
whatever your input is it Maps it
between 0 to 1 so even if your input is
as large as how thousands or lakhs earth
millions it will map it between 0 to 1
so that is very useful in usual networks
because when the input goes out of 0 it
may start to increase exponentially
which may be a problem so it contains
the input between 0 to 1 that is very
useful now we have time
activation function which contains the
function from minus 1 to 1 and is
similarly useful to the sigmoid function
you can try out between tan hedge and
sigmoid and check which accuracy is
better and use it accordingly
so this is the Spanish activation
function 2 by 1 plus E power minus 2x
minus 1 and this is the a span function
there is this return inverse effect so
even this contains the numbers between
somewhere around minus PI by 2 to plus
PI by 2 so even this is a kind of
replaceable and alternative to sigmoid
or tannish it does a similar task now we
have a value which is a very popular one
when it comes to deep learning and even
normal neural network so what it does is
whenever your function is less than 0 it
gives you 0 and whenever it is greater
than 0 it remains positive so what it
does is it removes the negative part of
your function and now we have leaky rule
which does a similar job but the it does
not make the negative input 0 just
reduces the magnitude of it and then we
have a soft Mac classifiers so this is
used to impart probabilities when you
have a 4 or 5 output and you pass it
through this you get the probability
distribution of E and this is useful for
finding out the most probable occurrence
or the classification where the
probability of a class is maximum so
these are some famous activation
functions which are very useful and play
an important role in deciding the
accuracy of your model whenever you have
a doubt that this activation function
might be better it's always better that
you use it and test B accuracy because
you never know which activation
nonsense we will give you the best
accuracy so that's it for now wait if
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