Machine learning can seem like a daunting field to get into. Within that field, artificial neural networks have a particularly intimidating reputation. Especially for those of us without a Ph.D. and years of AI experience. However, while the math that drives neural networks is fairly complex, putting them to practical use is surprisingly easy. As long as you know how to get started.
This session will cover how neural networks work without getting too bogged down in the details. The main focus will be on how to quickly and simply put them to practical use. Examples in this session with be built with the Python library, Keras: a high level neural networks API that boldly claims to be “designed for human beings”. By the end, you will be able to start using neural networks in your own projects and have a good understanding of the ecosystem of tools and libraries that help you work with neural networks.
Make neural networks accessible to all developers
Developers that want to start taking advantage of machine learning in their apps
Assumed Audience Knowledge
No experience with Python or machine learning is necessary
Five Things Audience Members Will Learn
- The types of problems neural networks can solve
- Why Python is a good fit for machine learning
- How to keep the complexities of neural networks at bay
- Libraries that are commonly used for machine learning
- How to tune a network to optimize accuracy