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Lecture 6: Infrastructure & Tooling (Full Stack Deep Learning - Spring 2021)
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Classroom Contents
Full Stack Deep Learning - Spring 2021
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- 1 Lecture 1: Deep Learning Fundamentals (Full Stack Deep Learning - Spring 2021)
- 2 Notebook: Coding a Neural Network (Full Stack Deep Learning - Spring 2021)
- 3 Lab 1: Setup and Intro (Full Stack Deep Learning - Spring 2021)
- 4 Lab 2: CNNs and Synthetic Data - Full Stack Deep Learning - Spring 2021
- 5 Lecture 2A: Convolutional Neural Networks (Full Stack Deep Learning - Spring 2021)
- 6 Lecture 2B: Computer Vision Applications (Full Stack Deep Learning - Spring 2021)
- 7 Lecture 3: Recurrent Neural Networks (Full Stack Deep Learning - Spring 2021)
- 8 Lab 3: RNNs (Full Stack Deep Learning - Spring 2021)
- 9 Lecture 4: Transfer Learning and Transformers (Full Stack Deep Learning - Spring 2021)
- 10 Lab 4: Transformers (Full Stack Deep Learning - Spring 2021)
- 11 Lecture 5: ML Projects (Full Stack Deep Learning - Spring 2021)
- 12 Lecture 6: Infrastructure & Tooling (Full Stack Deep Learning - Spring 2021)
- 13 Lab 5: Experiment Management (Full Stack Deep Learning - Spring 2021)
- 14 Lecture 7: Troubleshooting Deep Neural Networks (Full Stack Deep Learning - Spring 2021)
- 15 Lecture 8: Data Management (Full Stack Deep Learning - Spring 2021)
- 16 Lecture 9: Ethics (Full Stack Deep Learning - Spring 2021)
- 17 Lab 6: Data Labeling (Full Stack Deep Learning - Spring 2021)
- 18 Lab 7: Paragraph Recognition (Full Stack Deep Learning - Spring 2021)
- 19 Lecture 10: ML Testing & Explainability (Full Stack Deep Learning - Spring 2021)
- 20 Lab 8: Testing and Continuous Integration (Full Stack Deep Learning - Spring 2021)
- 21 Lecture 11B: Monitoring ML Models (Full Stack Deep Learning - Spring 2021)
- 22 Lecture 11A: Deploying ML Models (Full Stack Deep Learning - Spring 2021)
- 23 Lecture 12: Research Directions (Full Stack Deep Learning - Spring 2021)
- 24 Lab 9: Web Deployment (Full Stack Deep Learning - Spring 2021)
- 25 Panel Discussion: Do I need a PhD to work in ML? (Full Stack Deep Learning - Spring 2021)
- 26 Lecture 13: ML Teams (Full Stack Deep Learning - Spring 2021)
- 27 Top 10 Final Projects (Full Stack Deep Learning - Spring 2021)
