Pytorch tutorial: Loss functions

Deep Learning DIY by Marc Lelarge https://twitter.com/marc_lelarge -slides: https://dataflowr.github.io/website/modules/3-loss-functions-for-classification/ 0:00 Recap 2:25 How to choose your loss? 3:18 A probabilistic model for linear regression 7:50 Gradient descent, learning rate, SGD 11:30 Pytorch code for gradient descent 15:15 A probabilistic model for logistic regression 17:27 Notations (information theory) 20:58 Likelihood for logistic regression 22:43 BCELoss 23:41 BCEWithLogitsLoss 25:37 Beware of the reduction parameter 27:27 Softmax regression 30:52 NLLLoss 34:48 Classification in pytorch 36:36 Why maximizing accuracy directly is hard? 38:24 Classification in deep learning 40:50 Regression without knowing the underlying model 42:58 Overfitting in polynomial regression 45:20 Validation set 48:55 Notion of risk and hypothesis space 54:40 estimation error and approximation error - full course: https://www.dataflowr.com/