In deep learning, overfitting is a common problem that can hinder the performance and generalization of models. To address this issue, deep learning practitioners use a variety of techniques to regularize their models, including dropout, data augmentation, early stopping, L1 and L2 regularization, and batch normalization.

In this talk, we will explore the essential techniques for avoiding overfitting in deep learning, and discuss their benefits and limitations. Some potential questions to explore during the session include:

1. What is overfitting, and how does it affect the performance and generalization of deep learning models?
2. How does dropout work, and what are some best practices for using it effectively?
3. What are some common data augmentation techniques, and how can they help improve the performance and generalization of models?
4. What is early stopping, and how can it be used to prevent overfitting during training?
5. How do L1 and L2 regularization work, and how do they differ from each other?
6. What is batch normalization, and how can it help prevent overfitting in deep learning models?
7. How do these techniques fit into the broader landscape of deep learning regularization, and what are some emerging trends and challenges in this area?

By the end of this session, participants will have a solid understanding of the essential techniques for avoiding overfitting in deep learning, and will be able to apply these techniques to their own projects and research.

* Technical requirements: Familiarity with deep learning concepts
* Conferences: Suitable for AI, machine learning, and data science events.
* First public delivery: New offering.
* Target audience: Deep learning practitioners and researchers seeking to improve model performance and generalisation. Above Intermediate level.
* Session duration: Can be adapted to different time slots, ranging from brief talks to longer workshops.