v0.3.0

Breaking changes

• lr_finder() now by default divides the range between start_lr and end_lr into log-spaced intervals, following the fast.ai implementation. Cf. Sylvain Gugger's post: https://sgugger.github.io/how-do-you-find-a-good-learning-rate.html. The previous behavior can be achieved passing log_spaced_intervals=FALSE to the function. (#82, @skeydan)
• plot.lr_records() now in addition plots an exponentially weighted moving average of the loss (again, see Sylvain Gugger's post), with a weighting coefficient of 0.9 (which seems a reasonable value for the default setting of 100 learning-rate-incrementing intervals). (#82, @skeydan)

Documentation

• Many wording improvements in the getting started guides (#81 #94, @jonthegeek).

New features

• Added MixUp callback and helper loss function and functional logic. (#82, @skeydan).
• Added a luz_callback_gradient_clip inspired by FastAI's implementation. (#90)
• Added a backward argument to setup allowing one to customize how backward is called for the loss scalar value. (#93)
• Added the luz_callback_keep_best_model() to reload the weights from the best model after training is finished. (#95)