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008			210428s2021 sz | s |||| 0|eng d
020			_a9783030686406
024	7		_a10.1007/978-3-030-68640-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
100	1		_aGianfagna, Leonida _eautor _9681550
245	1	0	_aExplainable AI with Python _cby Leonida Gianfagna, Antonio Di Cecco
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publising _c2021
300			_a1 recurso en línea (VIII, 202 páginas) _b119 ilustraciones, 103 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
505	0		_a1 -- The Landscape -- 1.1 Examples of what Explainable AI is -- 1.1.1 Learning Phase -- 1.1.2 Knowledge Discovery -- 1.1.3 Reliability and Robustness -- 1.1.4 What have we learnt from the 3 examples -- 1.2 Machine Learning and XAI -- 1.2.1 Machine Learning tassonomy -- 1.2.2 Common Myths -- 1.3 The need for Explainable AI -- 1.4 Explainability and Interpretability: different words to say the same thing or not? -- 1.4.1 From World to Humans -- 1.4.2 Correlation is not causation -- 1.4.3 So what is the difference between interpretability and explainability? -- 1.5 Making Machine Learning systems explainable -- 1.5.1 The XAI flow -- 1.5.2 The big picture -- 1.6 Do we really need to make Machine Learning Models explainable? -- 1.7 Summary -- 1.8 References -- 2. Explainable AI: needs, opportunities and challenges -- 2.1 Human in the loop -- 2.1.1 Centaur XAI systems -- 2.1.2 XAI evaluation from "Human in The Loop perspective" -- 2.2 How to make Machine Learning models explainable -- 2.2.1 Intrinsic Explanations -- 2.2.2 Post-Hoc Explanations -- 2.2.3 Global or Local Explainability -- 2.3 Properties of Explanations -- 2.4 Summary -- 2.5 References -- 3 Intrinsic Explainable Models -- 3.1.Loss Function -- 3.2.Linear Regression -- 3.3.Logistic Regression -- 3.4.Decision Trees -- 3.5.K-Nearest Neighbors (KNN) -- 3.6.Summary -- 3.7 References -- 4. Model-agnostic methods for XAI -- 4.1 Global Explanations: permutation Importance and Partial Dependence Plot -- 4.1.1 Ranking features by Permutation Importance -- 4.1.2 Permutation Importance on the train set -- 4.1.3 Partial Dependence Plot -- 4.1.4 Properties of Explanations -- 4.2 Local Explanations: XAI with Shapley Additive explanations -- 4.2.1 Shapley Values: a game-theoretical approach -- 4.2.2 The first use of SHAP -- 4.2.3 Properties of Explanations -- 4.3 The road to KernelSHAP -- 4.3.1 The Shapley formula -- 4.3.2 How to calculate Shapley values -- 4.3.3 Local Linear Surrogate Models (LIME) -- 4.3.4 KernelSHAP is a unique form of LIME -- 4.4 Kernel SHAP and interactions -- 4.4.1 The NewYork Cab scenario -- 4.4.2 Train the Model with preliminary analysis -- 4.4.3 Making the model explainable with KernelShap -- 4.4.4 Interactions of features -- 4.5 A faster SHAP for boosted trees -- 4.5.1 Using TreeShap -- 4.5.2 Providing explanations -- 4.6 A naïve criticism to SHAP -- 4.7 Summary -- 4.8 References -- 5. Explaining Deep Learning Models -- 5.1 Agnostic Approach -- 5.1.1 Adversarial Features -- 5.1.2 Augmentations -- 5.1.3 Occlusions as augmentations -- 5.1.4 Occlusions as an Agnostic XAI Method -- 5.2 Neural Networks -- 5.2.1 The neural network structure -- 5.2.2 Why the neural network is Deep? (vs shallow) -- 5.2.3 Rectified activations (and Batch Normalization) -- 5.2.4 Saliency Maps -- 5.3 Opening Deep Networks -- 5.3.1 Different layer explanation -- 5.3.2 CAM (Class Activation Maps) and Grad-CAM -- 5.3.3 DeepShap / DeepLift -- 5.4 A critic of Saliency Methods -- 5.4.1 What the network sees -- 5.4.2 Explainability batch normalizing layer by layer -- 5.5 Unsupervised Methods -- 5.5.1 Unsupervised Dimensional Reduction -- 5.5.2 Dimensional reduction of convolutional filters -- 5.5.3 Activation Atlases: How to tell a wok from a pan -- 5.6 Summary -- 5.7 References -- 6.Making science with Machine Learning and XAI -- 6.1 Scientific method in the age of data -- 6.2 Ladder of Causation -- 6.3 Discovering physics concepts with ML and XAI -- 6.3.1 The magic of autoencoders -- 6.3.2 Discover the physics of damped pendulum with ML and XAI -- 6.3.3 Climbing the ladder of causation -- 6.4 Science in the age of ML and XAI -- 6.5 Summary -- 6.6 References -- 7. Adversarial Machine Learning and Explainability -- 7.1 Adversarial Examples (AE) crash course -- 7.1.2 Hands-on Adversarial Examples -- 7.2 Doing XAI with Adversarial Examples -- 7.3 Defending against Adversarial Attacks with XAI -- 7.4 Summary -- 7.5 References -- 8. A proposal for a sustainable model of Explainable AI -- 8.1 The XAI "fil rouge" -- 8.2 XAI and GDPR -- 8.2.1 FAST XAI -- 8.3 Conclusions -- 8.4 Summary -- 8.5 References -- Index.
520	3		_aThis 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.
988			_aSpringer_Computer_2021
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _aInteligencia artificial _9413115
650		7	_2embne _9161036 _aPython (Lenguaje de programación)
700	1		_aDi Cecco, Antonio _eautor _9681552
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-68640-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2022 _dz _eu _zSI