Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

• understanding what odds ratios and log odds ratios are
• figuring out we can interpret logits as log odds ratios
• mapping logits into probabilities using a sigmoid function
• defining a logistic regression as a simple neural network with a sigmoid
function in the output
• understanding the binary cross-entropy loss and its PyTorch implementation
nn.BCELoss()
• understanding the difference between nn.BCELoss() and
nn.BCEWithLogitsLoss()
• highlighting the importance of choosing the correct combination of the last
layer and loss function
• using PyTorch’s loss functions' arguments to handle imbalanced datasets
• configuring model, loss function, and optimizer for a classification problem
• training a model using the StepByStep class
• understanding that the validation loss may be lower than the training loss
• making predictions and mapping predicted logits to probabilities
• using a classification threshold to convert probabilities into classes
• understanding the definition of a decision boundary
• understanding the concept of separability of classes and how it’s related to
dimensionality
• exploring different classification thresholds and their effect on the confusion
matrix
• reviewing typical metrics for evaluating classification algorithms, like true and
false positive rates, precision, and recall
• building ROC and precision-recall curves out of metrics computed for multiple
thresholds
• understanding the reason behind the quirk of losing precision while raising the
classification threshold
• defining the best and worst possible ROC and PR curves
• using the area under the curve to compare different models
Wow! That’s a whole lot of material! Congratulations on finishing yet another big
262 | Chapter 3: A Simple Classification Problem