Sebastian Raschka • 4/4/2022

Losses Learned

Read Original

This article explores the implementation of cross-entropy loss in PyTorch for deep learning classifiers. It explains the differences between BCELoss, BCEWithLogitsLoss, NLLLoss, and CrossEntropyLoss, highlighting common implementation mistakes and numerical optimization considerations. The content includes practical quizzes and guidance for both binary and multiclass classification scenarios.

0 comments
#Pytorch #Cross Entropy Loss #Binary Classification
Losses Learned

Comments

No comments yet

Be the first to share your thoughts!

Browser Extension

Get instant access to AllDevBlogs from your browser

Top of the Week

1
Introducing GPT-5.1 for developers
Simon Willison • 6 votes
2
A simple explanation of the big idea behind public key cryptography
Richard Gendal Brown • 1 votes
3
Google Antigravity Exfiltrates Data
Simon Willison • 1 votes
4
🚀 AG-UI + Agent Framework + .NET + Aspire: Web-Enabling Your Intelligent Agents (Blog + Demo + Code!)
Bruno Capuano • 1 votes
5
Fix “This video format is not supported” on YouTube TV
David Walsh • 1 votes
6
Tooltip Components Should Not Exist
TkDodo Dominik Dorfmeister • 1 votes
7
llm-anthropic 0.22
Simon Willison • 1 votes
8
GPT-5.1 Instant and GPT-5.1 Thinking System Card Addendum
Simon Willison • 1 votes
9
Nano Banana can be prompt engineered for extremely nuanced AI image generation
Simon Willison • 1 votes
10
Hire Me in Japan
Dan Abramov • 1 votes