In optimization theory, a loss or cost function measures the distance between the fitting or predicted values and real values. For the majority of machine learning models, improving performance means minimizing the loss function. But for deep neural networks, performing gradient descent to minimize the loss function for every parameter can be prohibitively resource-consuming. Traditional approaches include manually deriving and coding, or implementing the neural model using syntactic and semantic constraints of a machine learning framework like TensorFlow. But what if it were possible to simply write down loss functions using a NumPy library and have the work done automatically?

In optimization theory, a loss or cost function measures the distance between the fitting or predicted values and real values. For the majority of machine learning models, improving performance means minimizing the loss function. But for deep neural networks, performing gradient descent to minimize the loss function for every parameter can be prohibitively resource-consuming. Traditional approaches include manually deriving and coding, or implementing the neural model using syntactic and semantic constraints of a machine learning framework like TensorFlow. But what if it were possible to simply write down loss functions using a NumPy library and have the work done automatically?