PyTorch Activation and Loss Functions

Explore a comprehensive lecture on PyTorch activation and loss functions, delivered by Yann LeCun as part of Alfredo Canziani's deep learning course. Dive into common activation functions, comparing those with kinks to smooth activations and understanding their impact on deep neural networks. Examine various loss functions in PyTorch, including margin-based losses and their applications. Learn how to design effective loss functions for Energy-Based Models (EBMs) and grasp the concept of "most offending incorrect answer." Gain insights through Q&A sessions and detailed explanations of topics such as AdaptiveLogSoftMax and CosineEmbeddingLoss. Access additional resources, including the course website and full YouTube playlist, to enhance your understanding of these crucial deep learning concepts.

– Week 11 – Lecture
– Activation Functions
– Q&A of activation
– Loss Functions until AdaptiveLogSoftMax
– Loss Functions until CosineEmbeddingLoss
– Loss Functions and Loss Functions for Energy Based Models
– Loss Functions for Energy Based Models