Book Image

Hands-On Explainable AI (XAI) with Python

By : Denis Rothman
Book Image

Hands-On Explainable AI (XAI) with Python

By: Denis Rothman

Overview of this book

Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex. Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications. You will build XAI solutions in Python, TensorFlow 2, Google Cloud’s XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces. By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI.
Table of Contents (16 chapters)
14
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15
Index

Defining and training the autoencoder

In this section, we will create, train, and test the autoencoder.

An autoencoder will encode the input data using a CNN with the same type of layers as the CNN we created in the Defining and training the CNN model section of this chapter.

However, there is a fundamental difference compared with the CNN:

An autoencoder encodes the data and then decodes the result to match the input data.

We are not trying to classify the inputs. We are finding a set of weights that guarantees that if we apply that set of weights to a perturbation of an image, we will remain close to the original.

The perturbations are ways to find the missing feature in an instance that produced a prediction. For example, in the The contrastive explanation method section of this chapter, we examined the following possible symptoms of the patient described as features:

Features for label 1 = {cough, fever, number of days=5}

The label of the diagnosis...