MyGrad is a simple, NumPy-centric math library that is capable of performing automatic differentiation. That is, the mathematical functions provided by MyGrad are capable of computing their own derivatives. If you know how to use NumPy then you can learn how to use MyGrad in a matter of minutes!

While fantastic auto-differentiation libraries like TensorFlow, PyTorch, and MXNet are available to the same end as MyGrad (and far beyond, ultimately), they are industrial-grade tools in both function and form. MyGrad’s primary purpose is to serve as an educational tool. It is simple to install (its only core dependency in NumPy), it is trivial to use if you are comfortable with NumPy, and its code base is well-documented and easy to understand. This makes it simple for students and teachers alike to use, hack, prototype with, and enhance MyGrad!

## Why is Automatic Differentiation Useful?¶

In general, auto-differentiation permits us to compute massive equations that depend on millions of variables and then seamlessly evaluate the derivatives of the equation’s output with respect to every one of those variables. This capability lies at the heart of the burgeoning field of deep learning, which is now the predominant use case for auto-differentiation libraries, and is the manifest purpose of TensorFlow, PyTorch, and MXNet.

The “decisions” made by a neural network are dictated by the network’s many, many parameters, which us researchers have arranged to serve as variables in a tremendous equation. This equation might, for example, attempt to take as input the pixels of a picture and return as an output an image-classification - a statement of the image’s content (e.g. 0 is ‘dog’, 1 is ‘cat’, etc.).

The way that we train this neural network is by “tuning” the values of its many parameters so that the network’s predictions reliably agree with what we know to be true. It turns out that having access to the derivative of the neural network’s output with respect to its parameters grants us the ability to quite reliably optimize its parameters - through a process known as gradient-based optimization we can update the values of these parameters to steer the neural network towards making more faithful predictions (note: a gradient is just a collection of derivatives of a multivariate function).

More specifically, we can hook our neural network up to an “objective” function that measures how well its predictions match against “the truth”. Recalling the basic definition of a derivative (as prescribed by any calculus course) and its relationship to the slope of a function at a point, knowing the derivative of this objective function with respect to one of our neural network’s parameters means that we know whether increasing this parameter will increase or decrease the output of the objective function; tuning the parameter so will affect the network’s output such that its prediction is in closer agreement with the truth than before. If we make such an adjustment to each of our neural network’s parameters and repeat this process many times over, using a wide variety of “training data” we may arrive at a configuration of network parameters that permits our neural network to faithfully classify pictures that we have never encountered before.

Thus auto-differentiation permits us to efficiently and automatically compute the derivatives of massive functions by way of simply coding the functions using the auto-differentiation software. This in turn, is what allows us nimbly design neural networks and objective functions, and to tune the parameters of our neural networks using derivative-based (or gradient-based) optimization schemes.

It should be noted that description of training neural networks, as presented here, only provides a narrow view of deep learning. Specifically, it describes the supervised learning of an image classification problem. While this is sufficient for conveying the utility of auto-differentiation software as a means for training neural networks, there is more nuiance to deep learning than is suggested here.