Practical Deep Learning Study Group

[Jenna59]

Practical Deep Learning for Coders 2022 part 1, recorded at the University of Queensland, covers topics such as how to:

• Build and train deep learning models for computer vision, natural language processing, tabular analysis, and collaborative filtering problems
• Create random forests and regression models
• Deploy models
• Use PyTorch, the world’s fastest growing deep learning software, plus popular libraries like fastai and Hugging Face

What you will learn

After finishing this course you will know:

• How to train models that achieve state-of-the-art results in:
• Computer vision, including image classification (e.g., classifying pet photos by breed)
• Natural language processing (NLP), including document classification (e.g., movie review sentiment analysis) and phrase similarity
• Tabular data with categorical data, continuous data, and mixed data
• Collaborative filtering (e.g., movie recommendation)
• How to turn your models into web applications, and deploy them
• Why and how deep learning models work, and how to use that knowledge to improve the accuracy, speed, and reliability of your models
• The latest deep learning techniques that really matter in practice
• How to implement stochastic gradient descent and a complete training loop from scratch

Schedule

1: Getting started - Tues. Sept 24th

Suggested Reading: Ch #1

2: Deployment - Tues. Oct 8th

Suggested Reading: Ch #2

3: Neural net foundations - Tues. Oct 22nd

Suggested Reading: Ch #4

4: Natural Language Processing (NLP) - Tues. Nov 5th

Suggested Reading: Ch#10

5: From-scratch model - Tues. Nov 19th

Suggested Reading: Ch #4 & Ch #9

6: Random forests - Tues. Dec 3rd

Suggested Reading: Ch #9

7: Collaborative filtering - Tues. Dec 17th

Suggested Reading: Ch #8

8: Convolutions (CNNs) - Tues. Jan 7th

Suggested Reading: Ch #13

9: Bonus: Data Ethics - Tues. Jan 21st

Suggested Reading: Ch #3

[Jenna59]

Week 1 Discussion Questions

1. Do you need these for deep learning?

• Lots of math T / F
• Lots of data T / F
• Lots of expensive computers T / F
• A PhD T / F

2. Draw a picture that summarizes Samuel's view of a machine learning model.

3. What is a validation set? What is a test set? Why do we need them?

4. What is overfitting? Provide an example.

5. What kinds of features do the early layers of a CNN find? How about the later layers?

[virtualandy]

Good questions! I will take a crack when I can re-review materials.

Another question for folks:

• Were you able to get a Notebook running with some of the examples? Either locally or on Google Colab or Kaggle?

If not, post here and someone can try to help you get setup.