Understanding how to create a deep learning neural network is an essential component of any data scientist's knowledge base. This talk covers some of the challenges that arise when training neural networks. It focuses on the problem of overfitting and its potential remedy: regularisation. Learners should have a basic understanding of linear algebra and calculus at a minimum level.

* Discover what overfitting means and how to recognise it in deep learning models
* Understand how to sample your data to reduce the likelihood of overfitting
* Learn about regularisation and its use as a remedy for overfitting

Intermediate level.

Assuming audience have a basic knowledge of neural network Concepts
In addition to this I'm assuming that you do have a basic understanding of linear algebra and calculus but we won't be using anything more complicated than a derivative and a vector product :)

I recommend to watch the video : Deep Learning for Beginners (From Basics to Neural Networks) : https://www.youtube.com/watch?v=IuNEfmuoEuM&list=PLqYDykjFMcnGPLPC7cBp9zqgXaRqoUJrC&ab_channel=Codementor