We use transfer learning to reuse a previously trained network on a new custom classification task to get near state-of-the-art accuracy in a matter of minutes. We then slice and dice the results to understand how well is it classifying. Along the way, we build a common machine learning pipeline, which is repurposed throughout the book.

On the note of transfer learning, we hear from Jeremy Howard, co-founder of fast.ai on how hundreds of thousands of students use transfer learning to jumpstart their AI journey.

Read the chapter online here.

Go through the code in the following order:

1. 1-building-a -custom-classifier-in-keras-with-transfer-learning.ipynb: We will build a custom classifier in Keras in 30 lines!
2. 2-analyzing-the-results.ipynb: With our trained model, we can analyze how it's performing over the validation dataset. Beyond the simpler accuracy metrics, looking at the actual images of mispredictions should give an intuition on whether the example was truly hard or if our model is not sophisticated enough.

Download the Cats and Dogs dataset from Kaggle and place it in the data directory. You may have to create an account on Kaggle in order to download the data.

Even if you are a first-time learner of deep learning or a hobbiyst, you can develop foundational knowledge through these resources that allow one to play with different training scenarios in the browser without the need to install any packages! It covers not only the theory but also helps to build the intuition to solve future problems.

Take a look at the table below outlining a few video series, online books, and browser-based tools that will help further your understanding of the subject matter.