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Regularization is not limited to methods like L1/L2 regularization which are specific versions of what you showed. Regularization is any technique that would prevent network from overfitting and help network to be more generalizable to unseen data. Some other techniques are Dropout, Early Stopping, Data Augmentation, limiting the capacity of network by ...


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Mean Absolute Error is nothing but the mean of absolute errors. If your model gave $n$ predictions $\{\hat{y}_i\}_{i = 1}^{n}$ against $n$ ground truths $\{y_i\}_{i = 1}^{n}$, then MAE is defines as follows $$MAE_{model} = \dfrac{\sum\limits_{i = 1}^{n} |y_i - \hat{y}_i|}{n} $$. Thus, MAE gives the average amount of error. So, the machine learning model with ...


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All modern frameworks for deep learning (PyTorch, Jax, Tensorflow) support automatic differentation. These operations can be easily implemented. Here I write, how it would look like in PyTorch: class Net(nn.Module): def __init__(self): super().__init__() self.a = nn.Parameter(torch.randn(1)) self.b = nn.Parameter(torch.randn(...


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