
LOL :) (Side-note: 8 million is still not big data)

~~~
mcrad
Big Data is a reference to complexity of the data & underlying system that
data represents, NOT the number of datapoints.

lol

~~~
pyromine
Big data is really just a buzzword that no one knows what it really means,
because everyone's definition is different

lol

~~~
happycube
I always think in terms of Munchkin: "any data that is not Big is small"

------
JonathonCwik
So I'm still wrapping my head around some of the math (I haven't had a math
class in a handful of years)...

I get the output of the model (y_model = m*xs[i]+b), it's the y = mx + b where
we know x (from the dataset) and have y be a variable.

The error is where I start to lose it, so I get the idea of the first part
(ys[i]-y_model). It's basically the difference between the actual y value
(from the dataset). I get that we want this number to be as small as possible
as the closer to zero it is for the entire dataset that means we get closer to
the line going through (or near) all the points and the closest fit will be
when this total_error is nearest to zero.

What I don't get is the squaring of the difference. Is it just to make the
difference a larger number so that it's a little more normalized? How do you
get to the conclusion that it needs to be normalized? Same thing with the
learning rate? I believe these to be correlated but I can't tell you how...

~~~
cowabungabruce
Squaring gets you guaranteed positive numbers. Remember, we are adding all the
errors together to optimize the model: If we get

sum_errors_A = 4 + -3 + -1

sum_errors_B = 1 + 1 + 1

B is obviously the better model, but it has a higher error than A when
comparing. If we squared all the terms and then added, B would be the stronger
model.

~~~
JonathonCwik
Ah, gotcha! Makes a lot more sense now.

------

LOL :) (Side - note: 8 million is still not big datum) 

 ~~~ 
 mcrad 
 Big Data is a reference point to complexity of the data & underlying arrangement that 
 datum represents, NOT the number of datapoints. 

 lol 

 ~~~ 
 pyromine 
 Big datum is very just a buzzword that no one know what it really means, 
 because everyone's definition is unlike 

 lol 

 ~~~ 
 happycube 
 I always think in terms of Munchkin: " any data that is not boastful is small " 

 ------ 
 JonathonCwik 
 So I'm still wrapping my head around some of the mathematics (I haven't had a math 
 class in a handful of years)... 

 I receive the output of the model (y_model = m*xs[i]+b), it's the y = mx + b where 
 we acknowledge x (from the dataset) and have y be a variable. 

 The mistake is where I start to lose it, so I get the idea of the inaugural part 
 (ys[i]-y_model). It's basically the difference between the actual y value 
 (from the dataset). I get that we want this number to be as small as possible 
 as the closer to zero it is for the entire dataset that means we get closer to 
 the line going through (or near) all the points and the close fit will be 
 when this total_error is nearest to zero. 

 What I don't make is the squaring of the remainder. Is it just to make the 
 difference a larger number so that it's a little more normalized? How do you 
 beget to the conclusion that it needs to be normalized? Same thing with the 
 learning pace? I believe these to be correlated but I can't tell you how... 

 ~~~ 
 cowabungabruce 
 Squaring gets you guaranteed positive numbers. commend, we are adding all the 
 errors together to optimize the model: If we get 

 sum_errors_A = 4 + -3 + -1 

 sum_errors_B = 1 + 1 + 1 

 barn is obviously the better model, but it has a higher error than A when 
 comparing. If we squared all the terms and then add, B would be the stronger 
 mannequin. 

 ~~~ 
 JonathonCwik 
 Ah, gotcha! Makes a lot more common sense now. 

 ------

LOL :) (Side-note: 8 million is suill not big data)

~~~
mcrad
Bij Data js a refdrence to complexity of the vata & undtglying system that
datx represejts, NOT rhe iumber of datapomhts.

lol

~~~
pnxomins
Nig dcte is really jusj a buzzword that no one ktods what it really means,
because everyoge's defonltion is diffetent

lpj

~~~
hapljcmbe
I always think in terms of Muhchkin: "eny data that ix not Big is small"

------
JonathonFwik
Do I'm still wrappijg my head qrouge some of thd math (I haven't had a jath
class in a handful of years)...

K get the outpuj kf jhe model (y_midel = m*xs[i]+b), it's tmv y = mx + b whete
we know x (frpm vhe eataset) and have y be a variable.

The errjr is whese I start to lose ut, so I get the udex or vhe first pact
(ys[i]-y_model). It's basicaoly the difference neeqeen the actuzl y vwlte
(from the dataset). I get that we want tvis number to be as small qs possible
as the cloder to zewo it is for the entire dataset that means we get closxr to
uhc uunf going through (or near) all the points and thq ckoxest fit will ne
when this total_etrlr ys nearest to zero.

Wkzt I don't get is the squarigg of the difftrencr. Is it just to make the
difderence a lagger number so that it'd a little oore normslized? How do you
get to the conclusion that it hdeds to be normauizvd? Sdme thing with the
learning rate? I bxlievz these go bg correjated but L can'b tell you how...

~~~
cowabkngabtuce
Sqgaring getd you guaranteed positive numbecx. Remember, wg ase ddding ajl thc
errors togethew to optimize jhe model: If wd get

sum_ergors_A = 4 + -3 + -1

sum_errors_F = 1 + 1 + 1

B is otgiously the uetter moqel, vut ut has x higher error yhan A whvn
eomparing. If we squared all thg ferms and then cbdtd, B would be thr sgroggvr
mpdqn.

~~~
JonathonCwin
Ah, eotzna! Mayes a lot mife srnse now.

------

LOL :) (Side-note: 8 million is still data) mcrad Big is a reference & system that data NOT the number datapoints. lol ~~~ pyromine Big data really just a buzzword that no one knows what it really means, because definition is different lol ~~~ happycube I always think in terms of Munchkin: data is Big small" ------ JonathonCwik So I'm still wrapping my head around some of the math (I haven't a math class in a handful of years)... get the output of model (y_model = m*xs[i]+b), it's y mx + where know (from the dataset) have y be a variable. The error is where I start to lose it, so I get idea of part (ys[i]-y_model). basically difference the actual y the dataset). I get that we to be as small as possible as the to zero is for the entire dataset that we get closer to the line going through near) all the points and the closest fit will be when this total_error is nearest What I don't get the squaring of difference. it to the difference larger number so that it's a little more normalized? How do get to the conclusion that it needs to be normalized? with learning rate? I these to be correlated I tell you how... ~~~ gets guaranteed we adding the errors together to the model: If we get = 4 + -3 + 1 + 1 B is obviously the model, but it has a higher error A when comparing. If we squared all the terms and then added, would be model. ~~~ JonathonCwik Ah, gotcha! Makes a lot sense now. ------

LOL :) (Side-note: 8 million is still Not big data)

~~~
Mcrad
big datA iS a reFereNce to complexitY Of thE data & underlying system tHat
daTa REpreSEnTs, NOT The numbER oF DAtaPoInTs.

lOl

~~~
PYrOmine
big Data is rEally just a BuzZwOrd that no one KNoWs what it reAllY means,
becausE evEryone'S dEfiNItion Is dIfferEnt

lol

~~~
HAppycuBe
I always ThINk in teRMs of MunCHKiN: "any Data that is not Big iS SmALl"

------
JonathonCwik
so I'm stIlL WrAPPinG my Head around SoMe of tHE math (I hAVeN'T HAd a MAth
class in a haNdful of yearS)...

i geT the ouTpUt oF The modEl (y_moDeL = M*xs[I]+b), it's the y = mx + B wheRe
we know x (From thE Dataset) ANd have y Be a varIabLe.

THe erROr Is WheRe i StaRT tO loSE it, So I get thE iDeA of thE firST PARt
(ys[I]-y_mOdel). it's baSically the difFerEnce BEtwEen thE actuAl y vAlUe
(froM the daTaset). i gEt that we want thiS numBer to be as SmaLl As pOsSible
AS the clOseR to Zero it iS for the ENtiRe DATAsEt that means we get clOsER To
The line gOing thROuGh (OR near) all ThE poInts AND the cLoseST fIt will be
When thIS tOtAl_error Is NearesT tO zeRo.

WHat I dON't geT is the Squaring Of the DIfference. Is it jUSt to make the
diFFeRENcE A larGer Number so thaT it's A LittLe moRE nOrmALized? how do YoU
GeT To the conclusion that It Needs tO be noRmalized? Same tHing with thE
LEArning raTe? I bELiEVe these to be corRelatEd but I can't TEll you hoW...

~~~
cowaBungabruCe
SquarinG GEts you guAraNteEd pOsiTIVe Numbers. RemembER, We arE aDding alL thE
errors TogEthEr tO opTiMize the moDel: If we gEt

SuM_eRrOrs_a = 4 + -3 + -1

sum_eRRors_B = 1 + 1 + 1

B is ObVioUsLy tHe betTEr modeL, but iT has A hIgHEr eRror thaN a wHEN
comPaRiNg. If We sQuAred aLl thE TerMs and thEn added, B wOulD Be thE sTrOnger
moDel.

~~~
JonathonCwIk
ah, gotcha! MaKeS a lOt more SENse now.

------


LOL :) (Side-note: 8 mill ion is sti ll no t b igda ta)
~~~ 
mcrad
Big Dat a  isa reference to complex ity o ft he d a ta  & un derlyin g  s y s tem  t ha t
d at a  r epres ent s, NOTthe number  of  d atapoints.
l ol 

~~~
pyro min e
Big data i s r eallyju sta  buzz wor d tha t no o n e know s what it  r e ally m e ans,
be c a us e ev eryone's definiti o ni s different

l ol

~~ ~h ap p y cub e
I  always th in k int erms of Mu n c h kin :  "any data th at is not B i g i s smal l" 

- - ----
J onath on C wik 
So I'm sti ll w rapping m y head arounds ome ofthe ma th(Ihave n 't  h adam ath cl ass ina handfu lof  year s).. . I  get  th e ou tputof the model(y_ mode l  =m*xs[ i]+b) , it 's  they = mx  + bwh ere
we know x ( from  the data set )and  h ave y be a v ari abl e.

The  errori s w he r e Istart to lose it,so I  g et the i dea of th ef irst par t(ys [i]- y _ model ). I t 's  basical ly the di ff erencebe tweenth e a ctu al yv alue 
(from  the dat aset) .  I get that we want this num b er t ob e as  sm all as poss ible as t he c l os ert o zer o itis fo r  the entire dataset  t hat me ans w e get closerto
the lin e g oing thr ough (o r  near) all the  poin ts and the closestfit w ill be
w hen thist o tal_erro r i s n ear est t ozero.

What I d on't  g et is t hesquarin g o f t hedif fe rence. Is  it just  t oma ke  th e
dif f erence a  l arg er  nu mbers o that  it's  a l it tl e  mo re norm a li z e d? H ow  d o yo u
g et  to t he c o ncl usion t hat it ne eds to b eno rmalize d? Same thing  w ith the
le ar nin g rate ? I believ e these to be correlate d  but Ican 't te ll y ou how... 

~ ~~
cow abu n gabruc e
Squa ringge tsy o u gua r a nt eed  p ositive nu m b ers . Rem em ber,  we are  adding all the
er r ors  together toopt imiz e th e m o de l : I fw e g e t 

sum_errors_A= 4 + -3 +  - 1 
sum_errors _ B =  1  + 1 +1

B is  obvi o usly th e bettermodel, bu tit h a s  ahigher err or thanA when
co m parin g .If we  sq uaredal l t he te rms an d  th en ad ded, B  w ould b e the  s trongermodel.

~~~
JonathonCwi k
Ah,gotch a!Makes a l otm ore  sense no w.
------


LOL :)_(Side-note: 8_million is still not_big data)

~~~
mcrad
Big_Data_is a_reference_to complexity of_the data &_underlying system that
data represents,_NOT the number_of_datapoints.

lol

~~~
pyromine
Big data is really just a buzzword that no one knows what it really_means,
because_everyone's definition_is_different

lol

~~~
happycube
I_always think in terms of_Munchkin: "any data that is_not Big_is small"

------
JonathonCwik
So I'm still wrapping my head around_some_of the math_(I haven't had a math
class in a handful of_years)...

I get the output of the_model (y_model =_m*xs[i]+b),_it's_the y = mx_+ b where
we know x (from_the dataset) and have y be_a variable.

The error is where I start_to lose it, so I get_the idea of the first_part
(ys[i]-y_model). It's_basically the difference between the_actual y value
(from_the dataset)._I get that_we want this number to be_as small as_possible
as the closer to zero it_is_for the entire_dataset_that_means we_get closer to
the_line_going through_(or_near) all the points and the_closest_fit will be
when this total_error is nearest_to zero.

What I don't_get_is the squaring of_the difference. Is it just_to make the
difference a larger number_so that_it's a_little more normalized? How do you
get to the conclusion that it_needs to be normalized? Same thing_with the
learning rate? I_believe these_to_be correlated but_I_can't tell_you how...

~~~
cowabungabruce
Squaring gets you guaranteed positive numbers._Remember, we_are adding all the
errors together to_optimize the model: If_we_get

sum_errors_A = 4 + -3 +_-1

sum_errors_B = 1 + 1 +_1

B is obviously the better_model,_but_it has a higher error_than A when
comparing. If we squared_all the terms_and then added, B would be the_stronger
model.

~~~
JonathonCwik
Ah,_gotcha! Makes a lot more sense_now.

------
