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This book provides a full presentation of the current concepts and
available techniques to make "machine learning" systems more
explainable. The approaches presented can be applied to almost all
the current "machine learning" models: linear and logistic
regression, deep learning neural networks, natural language
processing and image recognition, among the others. Progress in
Machine Learning is increasing the use of artificial agents to
perform critical tasks previously handled by humans (healthcare,
legal and finance, among others). While the principles that guide
the design of these agents are understood, most of the current
deep-learning models are "opaque" to human understanding.
Explainable AI with Python fills the current gap in literature on
this emerging topic by taking both a theoretical and a practical
perspective, making the reader quickly capable of working with
tools and code for Explainable AI. Beginning with examples of what
Explainable AI (XAI) is and why it is needed in the field, the book
details different approaches to XAI depending on specific context
and need. Hands-on work on interpretable models with specific
examples leveraging Python are then presented, showing how
intrinsic interpretable models can be interpreted and how to
produce "human understandable" explanations. Model-agnostic methods
for XAI are shown to produce explanations without relying on ML
models internals that are "opaque." Using examples from Computer
Vision, the authors then look at explainable models for Deep
Learning and prospective methods for the future. Taking a practical
perspective, the authors demonstrate how to effectively use ML and
XAI in science. The final chapter explains Adversarial Machine
Learning and how to do XAI with adversarial examples.
The book presents new models of axion for the solution of the
strong CP problem. The main feature of these models is the
modification of the relation between the axion mass and the
Peccei-Quinn(PQ) constant, with quite interesting phenomenological
consequences in particular for physics of supernovae, gamma ray
burst and cosmology. We have hypothesized the existence of another
sector of particles and interactions, the mirror world, which is an
exact copy of our world but with a larger electroweak scale. This
difference also implies a different dynamics in the QCD sector that
is fully discussed in the scenario of possible extensions of
Standard Model that are currently under validation with the last
generation of particle physics experiments.
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