Abstract
Deep learning uses statistical techniques to give computer systems the ability to learn with incoming data and to identify patterns and make decisions with minimal human direction. Armed with such targeted analytics, doctors may be better able to assess risk, make correct diagnoses, and offer patients more effective treatments. Deep Learning has a lot of potential in Healthcare. But why don’t these techniques are adopted in hospitals yet? What are the gaps between academic research and production level code in Deep Learning and Healthcare? How can we mitigate this production level gap in Deep Learning and Healthcare, and what are some of the tools and techniques we can deploy?
Date
Jan 20, 2021 7:30 PM
Event
Location
Virtual Talk
What was discussed in this talk?
- History of Deep Learning
- Why DeepLearing for Healthcare?
- Practical Limitations of Deep Learning
- Gap between Research and Production and how to mitigate?
- Data Agumentation
- Data Synthesis
- Pretraining
- Deep Learning as a Systemic Engineering
- Machine Learning Lifecycle and Infrastructure