29:00

Peter Welinder - Fireside Chat with OpenAI VP Product (LLM Bootcamp)

25:47

Harrison Chase - Agents Masterclass from LangChain Founder (LLM Bootcamp)

32:15

Reza Shabani - How Replit Trained Their Own LLMs (LLM Bootcamp)

49:11

LLMOps (LLM Bootcamp)

50:48

UX for Language User Interfaces (LLM Bootcamp)

39:33

Launch an LLM App in One Hour (LLM Bootcamp)

01:05:19

Augmented Language Models (LLM Bootcamp)

42:06

Project Walkthrough: askFSDL (LLM Bootcamp)

47:48

LLM Foundations (LLM Bootcamp)

01:00:09

What's Next? (LLM Bootcamp)

51:32

Learn to Spell: Prompt Engineering (LLM Bootcamp)

32:23

LangChain Demo + Q&A with Harrison Chase

01:25:02

Lecture 09: Ethics (FSDL 2022)

01:04:01

Lecture 08: ML Teams and Project Management (FSDL 2022)

01:00:02

Lecture 07: Foundation Models (FSDL 2022)

30:58

Lab 08: Monitoring (FSDL 2022)

01:05:18

Lecture 06: Continual Learning (FSDL 2022)

31:03

Lab 07: Web Deployment (FSDL 2022)

57:29

Lecture 05: Deployment (FSDL 2022)

15:16

Lab 06: Data Annotation (FSDL 2022)

34:53

Lecture 04: Data Management (FSDL 2022)

29:15

Lab 05: Troubleshooting & Testing (FSDL 2022)

42:42

Lecture 03: Troubleshooting & Testing (FSDL 2022)

32:10

Lab 04: Experiment Management (FSDL 2022)

51:52

Lecture 02: Development Infrastructure & Tooling (FSDL 2022)

13:17

Lab Intro and Overview (FSDL 2022)

10:44

Lab 03: Transformers and Paragraphs (FSDL 2022)

07:09

Lab 01: Neural networks in PyTorch (FSDL 2022)

08:37

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

53:34

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

01:29:56

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

36:20

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

58:14

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

20:26

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

01:20:02

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

53:26

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

36:55

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

13:26

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

01:41:12

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

18:40

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

01:04:51

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

05:06

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

59:43

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

01:07:27

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

30:41

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

01:07:22

Lecture 6: Infrastructure & Tooling (Full Stack Deep Learning - Spring 2021)

01:13:15

Lecture 5: ML Projects (Full Stack Deep Learning - Spring 2021)

10:36

Lab 4: Transformers (Full Stack Deep Learning - Spring 2021)

48:30

Lecture 4: Transfer Learning and Transformers (Full Stack Deep Learning - Spring 2021)

28:28

Lab 3: RNNs (Full Stack Deep Learning - Spring 2021)

01:06:11

Lecture 3: Recurrent Neural Networks (Full Stack Deep Learning - Spring 2021)

43:26

Lecture 2B: Computer Vision Applications (Full Stack Deep Learning - Spring 2021)

24:32

Lab 2: CNNs and Synthetic Data - Full Stack Deep Learning - Spring 2021

38:51

Lecture 2A: Convolutional Neural Networks (Full Stack Deep Learning - Spring 2021)

28:42

Lab 1: Setup and Intro (Full Stack Deep Learning - Spring 2021)

29:59

Lecture 1: Deep Learning Fundamentals (Full Stack Deep Learning - Spring 2021)

42:26

Notebook: Coding a Neural Network (Full Stack Deep Learning - Spring 2021)

48:02

Yangqing Jia on Deep Learning Frameworks (Full Stack Deep Learning - August 2018)

54:41

Andrej Karpathy on AI at Tesla (Full Stack Deep Learning - August 2018)

44:28

Jai Ranganathan at Data Science at Uber (Full Stack Deep Learning - August 2018)