Introductions to Derivatives
Derivatives help us understand how a function changes as its input changes. They measure the rate of change and are crucial in fields like physics, economics, and machine learning. By understanding derivatives, we can analyze trends, optimize processes, and predict behavior.
The Limit Definition of a Derivative
The derivative of a function f(x) at a specific point x=a is given by:
h→0limhf(x+h)−f(x)This formula tells us how much f(x) changes when we make a tiny step h along the x-axis. The smaller h becomes, the closer we get to the instantaneous rate of change.
Basic Derivative Rules
Power Rule
If a function is a power of x, the derivative follows:
dxdxn=nxn−1This means that when differentiating , we bring the exponent down and reduce it by one:
dxdx3=3x2Constant Rule
The derivative of any constant is zero:
dxdC=0For example, if f(x)=5, then:
dxd5=0Sum & Difference Rule
The derivative of a sum or difference of functions follows:
dxd[f(x)±g(x)]=f′(x)±g′(x)For example, differentiating separately:
dxd(x3+2x)=3x2+2Product & Quotient Rules
Product Rule
If two functions are multiplied, the derivative is found as follows:
dxd[f(x)g(x)]=f′(x)g(x)+f(x)g′(x)This means we differentiate each function separately and then sum their products. If f(x)=x2 and g(x)=ex, then:
dxd[x2ex]=2xex+x3exQuotient Rule
When dividing functions, use:
dxd[g(x)f(x)]=g(x)2f′(x)g(x)−f(x)g′(x)If f(x)=x2 and g(x)=x+1, then:
dxd[x+1x2]=(x+1)22x(x+1)−x2(1)Chain Rule: Differentiating Composite Functions
When differentiating nested functions, use:
dxdf(g(x))=f′(g(x))⋅g′(x)For example, if y=(3x+2)5, then:
dxd(3x+2)5=5(3x+2)4⋅3=15(3x+2)4This rule is essential in neural networks and machine learning algorithms.
Exponential Chain Rule Example:
When you're differentiating something like:
y=e2x2You're dealing with a composite function:
- Outer function: eu
- Inner function: u=2x2
Apply the chain rule step-by-step:
dxd2x2=4xThen multiply by the original exponential:
dxd(e2x2)=4x⋅e2x2In machine learning and neural nets, this shows up when working with exponential activations or loss functions.
Logarithmic Chain Rule Example:
Let's differentiate ln(2x). Again, it's a composite function — log on the outside, linear on the inside.
Differentiate the inner part:
dxd(2x)=2Now apply the chain rule to the log:
dxdln(2x)=2x1⋅2Which simplifies to:
dxdln(2x)=2x2=x1Even if you’re differentiating ln(kx), the result is always 1/x because the constants cancel out.
Special Case: Derivative of the Sigmoid Function
The sigmoid function is commonly used in machine learning:
σ(x)=1+x−x1Its derivative plays a key role in optimization:
σ′(x)=σ(x)(1−σ(x))If f(x)=1+e−x1, then:
f′(x)=(1+e−x)2e−xThis formula ensures that gradients remain smooth during training.
1. Which of the following correctly represents the derivative of x4?
2. The derivative of a constant is always:
3. What is the derivative of x5?
4. Given the following sigmoid function:
g(x)=1+e−x2What is its derivative g′(x)?
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Introductions to Derivatives
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Derivatives help us understand how a function changes as its input changes. They measure the rate of change and are crucial in fields like physics, economics, and machine learning. By understanding derivatives, we can analyze trends, optimize processes, and predict behavior.
The Limit Definition of a Derivative
The derivative of a function f(x) at a specific point x=a is given by:
h→0limhf(x+h)−f(x)This formula tells us how much f(x) changes when we make a tiny step h along the x-axis. The smaller h becomes, the closer we get to the instantaneous rate of change.
Basic Derivative Rules
Power Rule
If a function is a power of x, the derivative follows:
dxdxn=nxn−1This means that when differentiating , we bring the exponent down and reduce it by one:
dxdx3=3x2Constant Rule
The derivative of any constant is zero:
dxdC=0For example, if f(x)=5, then:
dxd5=0Sum & Difference Rule
The derivative of a sum or difference of functions follows:
dxd[f(x)±g(x)]=f′(x)±g′(x)For example, differentiating separately:
dxd(x3+2x)=3x2+2Product & Quotient Rules
Product Rule
If two functions are multiplied, the derivative is found as follows:
dxd[f(x)g(x)]=f′(x)g(x)+f(x)g′(x)This means we differentiate each function separately and then sum their products. If f(x)=x2 and g(x)=ex, then:
dxd[x2ex]=2xex+x3exQuotient Rule
When dividing functions, use:
dxd[g(x)f(x)]=g(x)2f′(x)g(x)−f(x)g′(x)If f(x)=x2 and g(x)=x+1, then:
dxd[x+1x2]=(x+1)22x(x+1)−x2(1)Chain Rule: Differentiating Composite Functions
When differentiating nested functions, use:
dxdf(g(x))=f′(g(x))⋅g′(x)For example, if y=(3x+2)5, then:
dxd(3x+2)5=5(3x+2)4⋅3=15(3x+2)4This rule is essential in neural networks and machine learning algorithms.
Exponential Chain Rule Example:
When you're differentiating something like:
y=e2x2You're dealing with a composite function:
- Outer function: eu
- Inner function: u=2x2
Apply the chain rule step-by-step:
dxd2x2=4xThen multiply by the original exponential:
dxd(e2x2)=4x⋅e2x2In machine learning and neural nets, this shows up when working with exponential activations or loss functions.
Logarithmic Chain Rule Example:
Let's differentiate ln(2x). Again, it's a composite function — log on the outside, linear on the inside.
Differentiate the inner part:
dxd(2x)=2Now apply the chain rule to the log:
dxdln(2x)=2x1⋅2Which simplifies to:
dxdln(2x)=2x2=x1Even if you’re differentiating ln(kx), the result is always 1/x because the constants cancel out.
Special Case: Derivative of the Sigmoid Function
The sigmoid function is commonly used in machine learning:
σ(x)=1+x−x1Its derivative plays a key role in optimization:
σ′(x)=σ(x)(1−σ(x))If f(x)=1+e−x1, then:
f′(x)=(1+e−x)2e−xThis formula ensures that gradients remain smooth during training.
1. Which of the following correctly represents the derivative of x4?
2. The derivative of a constant is always:
3. What is the derivative of x5?
4. Given the following sigmoid function:
g(x)=1+e−x2What is its derivative g′(x)?
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