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Intro
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Classroom Contents
Speeding Up the Deep Learning Development Life Cycle for Cancer Diagnostics
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- 1 Intro
- 2 Our Mission
- 3 Cancer diagnostics today
- 4 Future cancer diagnosis not for everyone?
- 5 Cancer diagnostics tomorrow
- 6 About MindPeak
- 7 Our Team and Advisors
- 8 Example: cancer cell detection
- 9 Simplicity
- 10 Training a deep learning model
- 11 Goal: Test new ideas quickly
- 12 Overview: Idea stage
- 13 Idea Generation - without data
- 14 Data-driven idea generation
- 15 Efficient Annotations
- 16 Metrics - define your target goals
- 17 Metrics - Mindpeak example
- 18 Overview: Implementation stage
- 19 Code quality-comments as code
- 20 Code quality - use einops library
- 21 On reproducibility
- 22 Implementation stage - summary
- 23 Overview: Training & Evaluation stage
- 24 PyTorch Data Parallelization
- 25 Pytorch Distributed Data Parallelization
- 26 Dataset reduction techniques
- 27 Training + evaluation stage - summary
- 28 Disappointment
