Video description
Sponsored by Amazon.
Understanding how to create a deep learning neural network is an essential component of any data scientist's knowledge base. This course covers some of the challenges that arise when training neural networks. It focuses on the problem of overfitting and its potential remedy: regularization. Learners should have a basic understanding of linear algebra and calculus.
- Discover what overfitting means and how to recognize it in deep learning models
- Understand how to sample your data to reduce the likelihood of overfitting
- Learn about regularization and its use as a remedy for overfitting
Laura Graesser is assisting in NVIDIA's autonomous driving project. Previously with The Boston Consulting Group, Laura is a graduate student at New York University, where she's working toward a master’s degree in computer science and machine learning. Laura's interests include neural networks and their application to computer vision problems, and the cross-fertilization between computer vision and natural language processing.
Table of contents
Product information
- Title: Avoiding the Pitfalls of Deep Learning: Solving Model Overfitting with Regularization and Dropout
- Author(s):
- Release date: November 2017
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781491999615
You might also like
book
Generative Deep Learning, 2nd Edition
Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and …
book
Deciphering Data Architectures
Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern …
book
Designing Data-Intensive Applications
Data is at the center of many challenges in system design today. Difficult issues need to …
book
Learning Algorithms
When it comes to writing efficient code, every software professional needs to have an effective working …