Neural networks achieve state-of-the-art accuracy in many fields such as computer vision, natural-language processing, and reinforcement learning. However, neural networks are complex, easily containing hundreds of thousands, or even, millions of operations (MFLOPs or GFLOPs). This complexity makes interpreting a neural network difficult. For example: How did the network arrive at the final prediction? Which parts of the input influenced the prediction? This lack of understanding is exacerbated for high-dimensional inputs like images: What does an explanation for an image classification even look like?

Research in Explainable AI (XAI) works to answers these questions with a number of different explanations. In this tutorial, you’ll specifically explore two types of explanations: 1. Saliency maps, which highlight the most important parts of the input image; and 2. decision trees, which break down each prediction into a sequence of intermediate decisions. For both of these approaches, you’ll produce code that generates these explanations from a neural network.

Along the way, you’ll also use deep-learning Python library PyTorch, computer-vision library OpenCV, and linear-algebra library numpy. By following this tutorial, you will gain an understanding of current XAI efforts to understand and visualize neural networks.

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