Solving real world data science problems with Python! (computer vision edition)

In this video we work on a real world computer vision problem using Python. The problem task is to create a model that can distinguish a flower known as “La Eterna” from other types of flowers. To do this we create convolutional neural networks (CNNs) using the Tensorflow/Keras libraries. We examine how to create a simple model and then improve it using techniques such as data augmentation & preprocessing. We play around with different types of network architectures and see how changes improve or decrease overall task performance. Link to source code (Github): Link to HP challenge: My previous videos on neural networks! Intro to neural nets: Real-world tutorial: *** I’ve left a bunch of additional useful resources in the README of the Github repo ***