Все публикации

0:29:15

Lab 05: Troubleshooting & Testing (FSDL 2022)

0:42:42

Lecture 03: Troubleshooting & Testing (FSDL 2022)

0:32:10

Lab 04: Experiment Management (FSDL 2022)

0:51:52

Lecture 02: Development Infrastructure & Tooling (FSDL 2022)

0:13:17

Lab Intro and Overview (FSDL 2022)

0:08:37

Lab 02: PyTorch Lightning and Convolutional NNs (FSDL 2022)

0:07:09

Lab 01: Neural networks in PyTorch (FSDL 2022)

0:10:44

Lab 03: Transformers and Paragraphs (FSDL 2022)

0:53:34

Lecture 01: When to Use ML and Course Vision (FSDL 2022)

1:29:56

Top 10 Final Projects (Full Stack Deep Learning - Spring 2021)

0:36:20

Panel Discussion: Do I need a PhD to work in ML? (Full Stack Deep Learning - Spring 2021)

0:58:14

Lecture 13: ML Teams (Full Stack Deep Learning - Spring 2021)

1:20:02

Lecture 12: Research Directions (Full Stack Deep Learning - Spring 2021)

0:20:26

Lab 9: Web Deployment (Full Stack Deep Learning - Spring 2021)

0:36:55

Lecture 11B: Monitoring ML Models (Full Stack Deep Learning - Spring 2021)

0:53:26

Lecture 11A: Deploying ML Models (Full Stack Deep Learning - Spring 2021)

0:13:26

Lab 8: Testing and Continuous Integration (Full Stack Deep Learning - Spring 2021)

0:18:40

Lab 7: Paragraph Recognition (Full Stack Deep Learning - Spring 2021)

1:41:12

Lecture 10: ML Testing & Explainability (Full Stack Deep Learning - Spring 2021)

1:04:51

Lecture 9: Ethics (Full Stack Deep Learning - Spring 2021)

0:05:06

Lab 6: Data Labeling (Full Stack Deep Learning - Spring 2021)

0:59:43

Lecture 8: Data Management (Full Stack Deep Learning - Spring 2021)

0:30:41

Lab 5: Experiment Management (Full Stack Deep Learning - Spring 2021)

1:07:27

Lecture 7: Troubleshooting Deep Neural Networks (Full Stack Deep Learning - Spring 2021)