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parameterss typo (#260)

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04_mnist_basics.ipynb
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@ -2651,7 +2651,7 @@
"source": [ "source": [
"The one magic step is the bit where we calculate the gradients. As we mentioned, we use calculus as a performance optimization; it allows us to more quickly calculate whether our loss will go up or down when we adjust our parameters up or down. In other words, the gradients will tell us how much we have to change each weight to make our model better.\n", "The one magic step is the bit where we calculate the gradients. As we mentioned, we use calculus as a performance optimization; it allows us to more quickly calculate whether our loss will go up or down when we adjust our parameters up or down. In other words, the gradients will tell us how much we have to change each weight to make our model better.\n",
"\n", "\n",
"You may remember from your high school calculus class that the *derivative* of a function tells you how much a change in its parameterss will change its result. If not, don't worry, lots of us forget calculus once high school is behind us! But you will have to have some intuitive understanding of what a derivative is before you continue, so if this is all very fuzzy in your head, head over to Khan Academy and complete the [lessons on basic derivatives](https://www.khanacademy.org/math/differential-calculus/dc-diff-intro). You won't have to know how to calculate them yourselves, you just have to know what a derivative is.\n", "You may remember from your high school calculus class that the *derivative* of a function tells you how much a change in its parameters will change its result. If not, don't worry, lots of us forget calculus once high school is behind us! But you will have to have some intuitive understanding of what a derivative is before you continue, so if this is all very fuzzy in your head, head over to Khan Academy and complete the [lessons on basic derivatives](https://www.khanacademy.org/math/differential-calculus/dc-diff-intro). You won't have to know how to calculate them yourselves, you just have to know what a derivative is.\n",
"\n", "\n",
"The key point about a derivative is this: for any function, such as the quadratic function we saw in the previous section, we can calculate its derivative. The derivative is another function. It calculates the change, rather than the value. For instance, the derivative of the quadratic function at the value 3 tells us how rapidly the function changes at the value 3. More specifically, you may recall that gradient is defined as *rise/run*, that is, the change in the value of the function, divided by the change in the value of the parameter. When we know how our function will change, then we know what we need to do to make it smaller. This is the key to machine learning: having a way to change the parameters of a function to make it smaller. Calculus provides us with a computational shortcut, the derivative, which lets us directly calculate the gradients of our functions." "The key point about a derivative is this: for any function, such as the quadratic function we saw in the previous section, we can calculate its derivative. The derivative is another function. It calculates the change, rather than the value. For instance, the derivative of the quadratic function at the value 3 tells us how rapidly the function changes at the value 3. More specifically, you may recall that gradient is defined as *rise/run*, that is, the change in the value of the function, divided by the change in the value of the parameter. When we know how our function will change, then we know what we need to do to make it smaller. This is the key to machine learning: having a way to change the parameters of a function to make it smaller. Calculus provides us with a computational shortcut, the derivative, which lets us directly calculate the gradients of our functions."
] ]