There are many ways to save and load models in TensorFlow and Keras. It’s good to have a range of options but sometimes with all of the flexibility it’s hard to know which one you actually need in the moment. This post demonstrates the different methods available and talks about the strengths of each.

In this notebook, we’re going to take the [dataset we prepared] and continue to iterate on the modeling. Last time we built a model using TensorFlow and Xception. This time, we’re going to iterate on that using FastAI.

In this notebook, we’re going to take our prepared images and augment them to increase the size of our dataset.

In this notebook, we’re going to take the dataset we prepared and build a model to classify the images.

In this series of posts, I will show how to build an image classifier using your own dataset. We’ll discuss how to prepare a dataset for machine learning, how to build a model to classify it, and techniques to improve the model’s performance. This post will focus on the data preparation.

This post shows some of the various tools in Python for visualizing images. There are usually two steps to the visualization process. First, you’ll need to read in the image from a file path, usually as a numpy array or something similar. Then, you can visualize it with various libraries.

Let’s look at how to explore images in Python. We’ll use the popular and active Pillow fork of PIL, the Python Imaging Library.