Deep Learning Hands-On Series – Model Tuning
This is the main work that is done in parallel with the engineering effort that is mostly happening elsewhere. In this tutorial we will walk through how to get better performance out of your model and a bunch of tips and tricks.
What you will learn
During this tutorial we will go through:
- bottleneck revisited
- loss functions
- parameter tuning
- dimensionality reduction
Who should attend
This workshop is for folks with a background in statistics, machine learning and specifically neural networks. We will be walking through a number of techniques for tuning networks.
Some knowledge of
- Machine Learning
Eric Schles is a senior data scientist with 6 years of full time experience. During his time in industry he has worked in the anti human trafficking, cancer research, government and big tech spaces. During his time at Microsoft he worked as a consulting engineer building production systems for fortune 500 and 100 clients all over the world. During his time in government he worked for the Federal Reserve in San Francisco, the White House and the General Services administration, bringing data science into the procurement process, health systems, human resource systems and in various inter agency consulting capacities. In addition, he worked on strategic cross federal initiatives such as the white house data council and various internal research goals to bring data science to federal agencies as well as assess readiness of agencies for data science.
Upkar Lidder, IBM Data Science and AI Developer Advocate, https://www.linkedin.com/in/lidderupk/