Index page

fastai Book

What I found interesting from the book "Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD" by Jeremy Howard (Author), Sylvain Gugger (Author)

Link to the book

I will not be adding any codes here as those will be part of the projects/deeper blog posts.

Drive train approach

• Define an objective
• Find out what inputs you can control
• Find out what data can be collected

Data issues

• Out of domain data
• Different lighting conditions
• Corrupted images
• Type of image the model sees change over time

Deplyoment

• The model itself might change behavior of the place where it is deployed
• Gradual expansion of users
• Parallel human process
• Consider what could go wrong
• Look for feedback loops

General points

• If it isnt an image, you can still make it one if you are creative xD
• Train a model, then use the trained model to find errors in data. Its faster.
• Sometimes things are just not solvable using Deep Learning or do not need it
• NLP : Not that great yet
• BLOG!! (Lol considering this is one)
• Keep checking shapes of layers/inputs as you go
• Large models arent necessarily better models. Start small and then scale up.

Ethics

• Well founded standards of right or wrong
• Context dependent
• Many perspectives of stakeholders
• Mistakes in software implementation
• Feedback loops -> might lead to worse outcomes/incorrect predictions
• "To the blind technocrat, the means were more important than the end"
• Consider the entire pipeline
• Work closely with people who will end up using the research

Technical/Math

• difference between L1 norm and mean squared error (MSE) is that the latter will penalize bigger mistakes more heavily than the former (and be more lenient with small mistakes).
• Regression -> Add y range. aka (min,max). that does better