2024-07-12
한어Русский языкEnglishFrançaisIndonesianSanskrit日本語DeutschPortuguêsΕλληνικάespañolItalianoSuomalainenLatina
| Columna attributum x *j | Numerus attributorum n | x ⃗ vec{x}x**************************(ego)row vector | certum valorem x ji vec{x}_j^ix**************************jegoSursum et deorsum |
|---|---|---|---|
| medium μ | standardisation | Standard declinatio σ | sigma（σ） |
w vec{w}w************** = [w1 w**2 w**3 …]
x ⃗ vec{x}x************************** = [x1 x**2 x**3 …]
fw ⃗ , b ( x ⃗ ) = w x + b = w 1 x 1 + w 2 x 2 + ... + wnxn + b f_{vec{w},b} (vec{x}) = vec{ w} * vec{x} + b = w_1x_1 + w_2x_2 + ... + w _nx_n + bf****w**************,b(x**************************)=w**************∗x**************************+b=w**************1x**************************1+w**************2x**************************2+…+w**************nx**************************n+b
import numpy
f = np.dot(w, x) + b
Nota: est velocissimum, cum n magnum est (parallel processus)
wn = wn − α 1 m i = 1 mfw ⃗ , b ( x ⃗ ( i ) − y ( i ) ) xn ( i ) w_n = w_n - αdfrac{1}{m} sumlimits_{i=}^mf_ {vec{w},b}(vec{x}^{(i)}-y^{(i)})x_n^{(i)}w**************n=w**************n−αm1ego=1∑mf****w**************,b(x**************************(ego)−y**(ego))x**************************n(ego)
b = b α 1 m i = 1 m ( fw , b ( x ⃗ ( i ) − y ( i ) ) b = b - α{dfrac{1}{m}} summae_{i=}^ m(f_{vec{w},b}(vec{x}^{(i)}-y^{(i)})b=b−αm1ego=1∑m(f****w**************,b(x**************************(ego)−y**(ego))
Pondera variabilibus independentibus respondentia in ampliori ambitu minora esse tendunt, et pondera independens variabilium correspondentia in minore ambitu maiora tendunt.
Divide per maximum valorem range ut pondus versus [0, 1] variabilis independens
Abscissa: x 1 = x 1 − μ 1 2000 − 300 x_1 = dfrac{x_1-μ_1}{2000-300}x**************************1=2000−300x**************************1−μ1 Y-axis: x 2 = x 2 − μ 2 5 − 0 x_2 = dfrac{x_2 - μ_2}{5-0}x**************************2=5−0x**************************2−μ2
0.18 ≤ x 1 ≤ 0.82 -0.18le x_1le0.82−0.18≤x**************************1≤0.82 0.46 ≤ x 2 ≤ 0.54 -0.46le x_2le0.54−0.46≤x**************************2≤0.54
300 ≤ x 1 ≤ 2000 300le x_1le2000300≤x**************************1≤2000 0 ≤ x 2 ≤ 5 0le x_2le50≤x**************************2≤5
x 1 = x 1 − μ 1 σ 1 x1 = dfrac{x_1-μ_1}{σ_1}x**************************1=σ1x**************************1−μ1 0.67 ≤ x 1 ≤ 3.1 -0.67le x_1le3.1−0.67≤x**************************1≤3.1
Conare valores omnium lineamentorum in simili ambitu per scalas conservare, ut ictum earum mutationum in valores praedictorum prope sit (-3,3).
Si sumptus munus J magnum fit, significat gradum magnitudinis (learning rate) ineptum esse vel codicem esse perperam.
Nota: numerus iterations variat ab machina ad machinam
Praeter curvas hauriendas ut iterationis punctum determinare, concursus latae sententiae etiam adhiberi potest
Sit ε aequalis
1
0
−
3
10^{-3}
10−3si decre- mentum J minor est quam hoc paucitas, confluendum putatur.
Aedificare pluma engineering per mutationem vel compositionem ad plura optiones
fw ⃗ , b ( x ) = w 1 x 1 + w 2 x 2 + w 3 x 3 + b f_{vec{w},b}(vec{x}) = w_1x_1+w_2x_2+w_3x_3+bf****w**************,b(x**************************)=w**************1x**************************1+w**************2x**************************2+w**************3x**************************3+b
Nota: regressio polynomialis adhiberi potest pro congruentibus linearibus et nonlinearibus
technologiae technologiae plus quam 30 annos operam dedit et in variis linguis proficit ut java, linux, javascript, php, css, etc. Multas contributiones in aperto fonte campo fecit elit documentorum statione ad communicandas quaestiones technologiarum progressus ad futuram referentiam
