Practical Deep Learning for Coders with fast.ai & PyTorch Book Chapter wise Summary Points

• This is completely based on Practical Deep Learning Fast.ai course

[Chapter 1] Your Deep Learning Journey

• A regression model is one that attempts to predict one or more numeric quantities, such a temperature or a location. Sometimes people use the word regression to refer to a particular kind of model called a linear regression model.

• The entire purpose of loss is to define a “measure of performance” that the training system can use to update weights automatically.But a metric is defined for human consumption, so a good metric is one that is easy for you to understand.

• Images from a time series dataset using a technique called Gramian Angular Difference Field.

why do we need train,val and test

• We use validation loss to kind of of tune the hyper-params. By doing so, we are indirectly using the val data. That’s why we need test data also.

• Train data is used to train the model. (Training Process)

• Val data is used to improve the model. (Modeling Process)

• Test data is used to only evaluate the model. Don’t see it or share, just use it for evaluation.

• the automatic training process with backpropagation, the more manual process of trying different hyperparameters between training sessions, and the assessment of our final result.

• If your data includes the date and you are building a model to use in the future, you will want to choose a continuous section with the latest dates as your validation set.

[Chapter 2] From Model to Production

• First try to Make and end to end pipeline for the project you want to work.
• Then iteratively modify the parts, like data is not enough or need to use another architecture etc.
• The Drivetrain Approach
  • Define your objective like what outcome do you want.
  • Define the actions which you can do to get there like what inputs you can control.
  • What type of data can help or you can collect.
  • Then build a model that you can use to determine the best actions to take to get the best results in terms of your objective.