Loss Functions

Lesson in the Machine Learning Roadmap · Stage 31: Convolutional Neural Network

Last updated 14 September 2026

What is Loss Functions?

Loss functions measure how well the network's predictions match the actual values. They quantify the difference between the predicted output and the true output for a given input. The goal during training is to minimize this loss, guiding the network to adjust its internal parameters (weights and biases) to make more accurate predictions. Different loss functions are suitable for different types of problems, such as regression or classification.

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Free resources to learn Loss Functions

ArticleLoss Functions and Their Use In Neural Networks
ArticleWhat is Loss Function? | IBM
VideoLoss in a Neural Network explained

Loss Functions: common questions

Can I learn Loss Functions in Hindi?

This lesson is written in English. Once you sign in, open it in the interactive course and ask the AI tutor to explain Loss Functions in Hindi or Hinglish. The tutor answers from this lesson’s own text, so it stays on the syllabus, and it runs on your own Google, OpenAI or Anthropic key.

Is this Loss Functions tutorial for beginners?

It is lesson 128 of 150 in the free Machine Learning roadmap, in stage 31, Convolutional Neural Network. If you are new to Machine Learning, start from stage 1 and come back here in order; if you already know the earlier stages, this lesson stands on its own.