Creating a custom image classification model is challenging, but the existence of neural network libraries like Keras has made it doable. Here's how, with many code samples and a full project download ...

The image classification algorithm takes an image as input and outputs a probability for each provided class label. Training datasets must consist of images in .jpg, .jpeg, or .png format.

Discover how to build an automated intent classification model by leveraging pre-training data using a BERT encoder, BigQuery, and Google Data Studio.

Fortunately, with ample spare time, those who share my problem can now use an image captioning model in TensorFlow to caption their photos and put an end to the pesky first-world problem.