W&B Fastbook Reading Group — 3. DL Models in Production & Ethical Considerations

1

0. What is machine learning?

Weights & Biases

2

1. Build Your First Machine Learning Model

Weights & Biases

3

Intro to ML: Course Overview

Weights & Biases

4

2. Multi-Layer Perceptrons

Weights & Biases

5

3. Convolutional Neural Networks

Weights & Biases

6

Weights & Biases at OpenAI

Weights & Biases

7

Why Experiment Tracking is Crucial to OpenAI

Weights & Biases

8

4. Autoencoders

Weights & Biases

9

5. Sentiment Analysis

Weights & Biases

10

6. Recurrent Neural Networks [RNNs]

Weights & Biases

11

7. Text Generation using LSTMs and GRUs

Weights & Biases

12

8. Text Classification Using Convolutional Neural Networks

Weights & Biases

13

9. Hybrid LSTMs [Long Short-Term Memory]

Weights & Biases

14

Toyota Research Institute on Experiment Tracking with Weights & Biases

Weights & Biases

15

Weights and Biases - Developer Tools for Deep Learning

Weights & Biases

16

Introducing Weights & Biases

Weights & Biases

17

10. Seq2Seq Models

Weights & Biases

18

11. Transfer Learning for Domain-Specific Image Classification with Small Datasets

Weights & Biases

19

12. One-shot learning for teaching neural networks to classify objects never seen before

Weights & Biases

20

13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow

Weights & Biases

21

14. Data Augmentation | Keras

Weights & Biases

22

15. Batch Size and Learning Rate in CNNs

Weights & Biases

23

Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)

Weights & Biases

24

Grading Rubric for AI Applications with Sergey Karayev (2019)

Weights & Biases

25

16. Video Frame Prediction using CNNs and LSTMs (2019)

Weights & Biases

26

Image to LaTeX - Applied Deep Learning Fellowship (2019)

Weights & Biases

27

17. Build and Deploy an Emotion Classifier (2019)

Weights & Biases

28

Applied Deep Learning - Data Management with Josh Tobin (2019)

Weights & Biases

29

Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)

Weights & Biases

30

Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)

Weights & Biases

31

Troubleshooting and Iterating ML Models with Lee Redden (2019)

Weights & Biases

32

Designing a Machine Learning Project with Neal Khosla (2019)

Weights & Biases

33

Lukas Beiwald on ML Tools and Experiment Management (2019)

Weights & Biases

34

Building Machine Learning Teams with Josh Tobin (2019)

Weights & Biases

35

Pieter Abeel on Potential Deep Learning Research Directions (2019)

Weights & Biases

36

Testing and Deployment of Deep Learning Models with Josh Tobin (2019)

Weights & Biases

37

Five Lessons for Team-Oriented Research with Peter Welder (2019)

Weights & Biases

38

Applied Deep Learning - Rosanne Liu on AI Research (2019)

Weights & Biases

39

Making the Mid-career Leap from Urban Design to Deep Learning/Data Science

Weights & Biases

40

Organizing ML projects — W&B walkthrough (2020)

Weights & Biases

41

Brandon Rohrer — Machine Learning in Production for Robots

Weights & Biases

42

Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars

Weights & Biases

43

My experiments with Reinforcement Learning with Jariullah Safi

Weights & Biases

44

Applications of Machine Learning to COVID-19 Research with Isaac Godfried

Weights & Biases

45

VDLS Lavanya Product Walkthrough

Weights & Biases

46

Testing Machine Learning Models with Eric Schles

Weights & Biases

47

How Linear Algebra is not like Algebra with Charles Frye

Weights & Biases

48

Predicting Protein Structures using Deep Learning with Jonathan King

Weights & Biases

49

Rachael Tatman — Conversational AI and Linguistics

Weights & Biases

50

Reformer by Han Lee

Weights & Biases

51

Sequence Models with Pujaa Rajan

Weights & Biases

52

GitHub Actions & Machine Learning Workflows with Hamel Husain

Weights & Biases

53

Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye

Weights & Biases

54

Jack Clark — Building Trustworthy AI Systems

Weights & Biases

55

Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye

Weights & Biases

56

Track your machine learning experiments locally, with W&B Local - Chris Van Pelt

Weights & Biases

57

Antipatterns in open source research code with Jariullah Safi

Weights & Biases

58

Attention for time series forecasting & COVID predictions - Isaac Godfried

Weights & Biases

59

Made with ML - Goku Mohandas

Weights & Biases

60

Angela & Danielle — Designing ML Models for Millions of Consumer Robots

Weights & Biases