You may know how to build a CNN - but do you understand it?

Most resources start with pristine datasets, start at importing and finish at validation. There's much more to know.

Why was a class predicted? Where was the model wrong? What made it predict wrong? What was the model's attention on? What do the learned features look like? Can we separate them into distinct classes? How close are the features that make Class N to the feature that make Class M?

Here's the latent feature space of your model visualized, and otherwise hidden away from you:

You're new to Deep Learning for Computer Vision and have gone through the theory and basics or you feel unsure about how to build Convolutional Neural Networks?

Literature is vast, and either it's too long and theoretical or too brief to be practical. In this Guided Project - we'll go through the process of building your own CNN using Keras, assuming you're familiar with the fundamentals.

In this project, through a practical, hand-held approach, you'll learn about:

Co-occurrence and the source of co-occurrence bias in datasets
Finding, downloading datasets, and extracting data
Visualizing subsets of images
Data loading and preprocessing
Promises and perils of Data Augmentation and Keras' ImageDataGenerator class
Defining a custom CNN architecture
Implementing LRFinder with Keras and finding learning rates automatically
Evaluating a model's classification abilities
Interpreting a model's predictions and evaluating errors
What makes the network predict wrong
Interpreting a model's attention maps to identify what models actually learn with tf-keras-vis and GradCam++
Interpreting what the model's convolutional layers have learned through Principal Component Analysis and t-SNE
How similarity search engines find similar images

Note: This Guided Project is part of our in-depth course on Practical Deep Learning for Computer Vision and assumes that you've read the previous lessons or have that prerequisite knowledge from before.

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