Interested to Buy Any Domain ? << Click Here >> for more details...
What are the differences between L1 and L2 regularization?
- 1 Answers
- 121 Views
- I also Faced
- E-Mail Answers
Answer Posted / Saumitra Kumar Mishra
L1 and L2 regularization are techniques used to prevent overfitting in machine learning models by adding a penalty term to the loss function. The key difference lies in the type of penalty: L1 regularization uses an absolute value (|w|) of the weights, while L2 regularization employs the square (w^2) of the weights. L1 regularization tends to produce sparse solutions (i.e., zeroing out some coefficients), whereas L2 regularization results in more continuous solutions.
| Is This Answer Correct ? | 0 Yes | 0 No |
Post New Answer View All Answers
What are the advantages of running AI models on IoT devices?
How do low-power AI models work in constrained environments?
How can you optimize AI models for edge deployment?
What are your strengths and weaknesses in AI?
What are the biggest challenges you see in AI implementation across industries?
How can you detect bias in AI models?
What frameworks can you use for ethical AI development?
Explain the difference between supervised, unsupervised, and reinforcement learning.
What is model interpretability, and why is it important?
What are the challenges in applying AI to environmental issues?
Explain the concept of SHAP and its role in XAI.
What challenges arise when implementing AI in finance?
Explain the concept of adversarial attacks and how to protect AI models from them.
What methods are used to make AI decisions more transparent?
What techniques can be used to make AI models more fair?
