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Machine Learning Viz
By
Charles Redmond
Edited
Jul 5, 2023
Fork of
Approximation of the Derivative
Machine Learning Viz
Forward Pass Illustration
Chain Rule Visualization
Approximation of the Derivative (Example)
Approximation of the Derivative
Derivative of Polynomial
Derivative of the Sigmoid
Derivative of Sine
The Derivative
Sigmoid Part 2
Sigmoid Function
ReLU
Hyperbolic Tangent
Logarithmic Function
Exponential Function
Polynomials
Lines
Line:Zero Intercept
Horizontal Lines
Insert cell
Insert cell
Insert cell
f
=
x
=>
x
/
(
x
**
2
+
1
)
Insert cell
fprime
=
x
=>
(
-
(
x
**
2
)
+
1
)
/
(
x
**
2
+
1
)
**
2
Insert cell
approx
=
x
=>
(
f
(
x
+
delta
)
-
f
(
x
-
delta
)
)
/
(
2
*
delta
)
Insert cell
Plot
.
plot
(
{
x
:
{
domain
:
[
-
10
,
10
]
}
,
y
:
{
domain
:
[
-
1
,
1
]
}
,
marks
:
[
Plot
.
ruleX
(
[
0
]
)
,
Plot
.
ruleY
(
[
0
]
)
,
Plot
.
line
(
x
,
{
x
:
x
,
y
:
x
.
map
(
x
=>
f
(
x
)
)
,
stroke
:
"red"
}
)
]
}
)
Insert cell
Plot
.
plot
(
{
x
:
{
domain
:
[
-
10
,
10
]
}
,
y
:
{
domain
:
[
-
1
,
1
]
}
,
marks
:
[
Plot
.
ruleX
(
[
0
]
)
,
Plot
.
ruleY
(
[
0
]
)
,
Plot
.
line
(
x
,
{
x
:
x
,
y
:
x
.
map
(
x
=>
fprime
(
x
)
)
,
stroke
:
"red"
}
)
]
}
)
Insert cell
viewof
delta
=
Inputs
.
range
(
[
.00001
,
10
]
)
Insert cell
Plot
.
plot
(
{
x
:
{
domain
:
[
-
10
,
10
]
}
,
y
:
{
domain
:
[
-
1
,
1
]
}
,
marks
:
[
Plot
.
ruleX
(
[
0
]
)
,
Plot
.
ruleY
(
[
0
]
)
,
Plot
.
line
(
x
,
{
x
:
x
,
y
:
x
.
map
(
x
=>
approx
(
x
)
)
,
stroke
:
"red"
}
)
]
}
)
Insert cell
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approx
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