For Machine Learning

We’ll talk about some major components within this library. Then we’re going to present a demo to showcase how DataPipe and DataLoader2 work. We will start off by showing you how to load data with the built-in Datapipe provided by TorchData.

This process is callable nominalization with attributes having a rescaled range of 0 and 1. It ensures the existence of an optimization algorithm that forms the core of gradient descent -an exam of the learning algorithm.

The transformers API makes it possible to save all of these pieces to disk at once, saving everything into a single archive in the PyTorch or TensorFlow saved model format.

PyTorch would compute the derivatives of the loss throughout the chain of functions (the computation graph) and accumulate their values in the grad attribute of those tensors.

You can be fairly sure that the model is using two-node binary classification because multi-class classification would have three or more output nodes and one-node binary classification would have one output node

In multi-label classification, we use a binary classifier where each neuron(y_train.shape[1]) in the output layer is responsible for one vs all class classification. binary_crossentropy is suited for binary classification and thus used for multi-label classification.

The ReLU activation function is a default choice for the hidden layers. For the output layer, in general, you will want the logistics activation function for binary classification, the softmax activation function for multiclass classification, and no activation function for regression.

Common deep learning libraries only implement L2 regularization, not the original weight decay. Therefore, on datasets where the use of L2 regularization is beneficial for SGD on many popular image classification datasets, Adam leads to worse results than SGD with momentum for which L2 regularization behaves as expected.

Fraction of the training data to be used as validation data. The Keras will set apart this fraction of the training data. The validation data is selected from the last samples in the x and y data provided.

Stratified train_test_split to maintain the imbalance so that the test and train dataset have the same distribution, then never touch the test set again. Stratified ensure that each dataset split has the same proportion of observations with a given label.