Practical explainable AI using Python: artificial intelligence model explanations using Python-based libraries, extensions, and frameworks
Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Berkeley, CA
Apress L.P.
2022
|
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781484271582/?ar |
| Zusammenfassung: | Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers. You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decision Further, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, you will be introduced to model explainability for unstructured data, classification problems, and natural language processing-related tasks. Additionally, the book looks at counterfactual explanations for AI models. Practical Explainable AI Using Python shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks. What You'll Learn Review the different ways of making an AI model interpretable and explainable Examine the biasness and good ethical practices of AI models Quantify, visualize, and estimate reliability of AI models Design frameworks to unbox the black-box models Assess the fairness of AI models Understand the building blocks of trust in AI models Increase the level of AI adoption Who This Book Is For AI engineers, data scientists, and software developers involved in driving AI projects/ AI products. |
| Beschreibung: | Includes index. - Print version record |
| Umfang: | 1 Online-Ressource (356 Seiten) |
| ISBN: | 9781484271582 1484271580 |
Internformat
MARC
| LEADER | 00000cam a22000002 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-072486244 | ||
| 003 | DE-627-1 | ||
| 005 | 20240228121541.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 220112s2022 xx |||||o 00| ||eng c | ||
| 020 | |a 9781484271582 |c electronic bk. |9 978-1-4842-7158-2 | ||
| 020 | |a 1484271580 |c electronic bk. |9 1-4842-7158-0 | ||
| 035 | |a (DE-627-1)072486244 | ||
| 035 | |a (DE-599)KEP072486244 | ||
| 035 | |a (ORHE)9781484271582 | ||
| 035 | |a (DE-627-1)072486244 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 072 | 7 | |a COM004000 |2 bisacsh | |
| 082 | 0 | |a 006.3 |2 23 | |
| 100 | 1 | |a Mishra, Pradeepta |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Practical explainable AI using Python |b artificial intelligence model explanations using Python-based libraries, extensions, and frameworks |c Pradeepta Mishra |
| 264 | 1 | |a Berkeley, CA |b Apress L.P. |c 2022 | |
| 300 | |a 1 Online-Ressource (356 Seiten) | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Includes index. - Print version record | ||
| 520 | |a Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers. You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decision Further, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, you will be introduced to model explainability for unstructured data, classification problems, and natural language processing-related tasks. Additionally, the book looks at counterfactual explanations for AI models. Practical Explainable AI Using Python shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks. What You'll Learn Review the different ways of making an AI model interpretable and explainable Examine the biasness and good ethical practices of AI models Quantify, visualize, and estimate reliability of AI models Design frameworks to unbox the black-box models Assess the fairness of AI models Understand the building blocks of trust in AI models Increase the level of AI adoption Who This Book Is For AI engineers, data scientists, and software developers involved in driving AI projects/ AI products. | ||
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Artificial intelligence |x Data processing | |
| 650 | 0 | |a Application software |x Development | |
| 650 | 4 | |a Python (Langage de programmation) | |
| 650 | 4 | |a Intelligence artificielle ; Informatique | |
| 650 | 4 | |a Logiciels d'application ; Développement | |
| 650 | 4 | |a Application software ; Development | |
| 650 | 4 | |a Artificial intelligence ; Data processing | |
| 650 | 4 | |a Python (Computer program language) | |
| 776 | 1 | |z 9781484271575 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9781484271575 |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/9781484271582/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
| 912 | |a ZDB-30-ORH | ||
| 912 | |a ZDB-30-ORH | ||
| 951 | |a BO | ||
| 912 | |a ZDB-30-ORH | ||
| 049 | |a DE-91 | ||
Datensatz im Suchindex
| DE-BY-TUM_katkey | ZDB-30-ORH-072486244 |
|---|---|
| _version_ | 1821494825805938688 |
| adam_text | |
| any_adam_object | |
| author | Mishra, Pradeepta |
| author_facet | Mishra, Pradeepta |
| author_role | aut |
| author_sort | Mishra, Pradeepta |
| author_variant | p m pm |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)072486244 (DE-599)KEP072486244 (ORHE)9781484271582 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03682cam a22004932 4500</leader><controlfield tag="001">ZDB-30-ORH-072486244</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240228121541.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220112s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781484271582</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">978-1-4842-7158-2</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1484271580</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">1-4842-7158-0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)072486244</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP072486244</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)9781484271582</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)072486244</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM004000</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.3</subfield><subfield code="2">23</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Mishra, Pradeepta</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Practical explainable AI using Python</subfield><subfield code="b">artificial intelligence model explanations using Python-based libraries, extensions, and frameworks</subfield><subfield code="c">Pradeepta Mishra</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Berkeley, CA</subfield><subfield code="b">Apress L.P.</subfield><subfield code="c">2022</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (356 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Includes index. - Print version record</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers. You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decision Further, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, you will be introduced to model explainability for unstructured data, classification problems, and natural language processing-related tasks. Additionally, the book looks at counterfactual explanations for AI models. Practical Explainable AI Using Python shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks. What You'll Learn Review the different ways of making an AI model interpretable and explainable Examine the biasness and good ethical practices of AI models Quantify, visualize, and estimate reliability of AI models Design frameworks to unbox the black-box models Assess the fairness of AI models Understand the building blocks of trust in AI models Increase the level of AI adoption Who This Book Is For AI engineers, data scientists, and software developers involved in driving AI projects/ AI products.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Python (Computer program language)</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Artificial intelligence</subfield><subfield code="x">Data processing</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Application software</subfield><subfield code="x">Development</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Python (Langage de programmation)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Intelligence artificielle ; Informatique</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Logiciels d'application ; Développement</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Application software ; Development</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence ; Data processing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Python (Computer program language)</subfield></datafield><datafield tag="776" ind1="1" ind2=" "><subfield code="z">9781484271575</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">9781484271575</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-ORH</subfield><subfield code="q">TUM_PDA_ORH</subfield><subfield code="u">https://learning.oreilly.com/library/view/-/9781484271582/?ar</subfield><subfield code="m">X:ORHE</subfield><subfield code="x">Aggregator</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
| id | ZDB-30-ORH-072486244 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-17T11:20:32Z |
| institution | BVB |
| isbn | 9781484271582 1484271580 |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (356 Seiten) |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Apress L.P. |
| record_format | marc |
| spelling | Mishra, Pradeepta VerfasserIn aut Practical explainable AI using Python artificial intelligence model explanations using Python-based libraries, extensions, and frameworks Pradeepta Mishra Berkeley, CA Apress L.P. 2022 1 Online-Ressource (356 Seiten) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes index. - Print version record Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers. You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decision Further, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, you will be introduced to model explainability for unstructured data, classification problems, and natural language processing-related tasks. Additionally, the book looks at counterfactual explanations for AI models. Practical Explainable AI Using Python shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks. What You'll Learn Review the different ways of making an AI model interpretable and explainable Examine the biasness and good ethical practices of AI models Quantify, visualize, and estimate reliability of AI models Design frameworks to unbox the black-box models Assess the fairness of AI models Understand the building blocks of trust in AI models Increase the level of AI adoption Who This Book Is For AI engineers, data scientists, and software developers involved in driving AI projects/ AI products. Python (Computer program language) Artificial intelligence Data processing Application software Development Python (Langage de programmation) Intelligence artificielle ; Informatique Logiciels d'application ; Développement Application software ; Development Artificial intelligence ; Data processing 9781484271575 Erscheint auch als Druck-Ausgabe 9781484271575 |
| spellingShingle | Mishra, Pradeepta Practical explainable AI using Python artificial intelligence model explanations using Python-based libraries, extensions, and frameworks Python (Computer program language) Artificial intelligence Data processing Application software Development Python (Langage de programmation) Intelligence artificielle ; Informatique Logiciels d'application ; Développement Application software ; Development Artificial intelligence ; Data processing |
| title | Practical explainable AI using Python artificial intelligence model explanations using Python-based libraries, extensions, and frameworks |
| title_auth | Practical explainable AI using Python artificial intelligence model explanations using Python-based libraries, extensions, and frameworks |
| title_exact_search | Practical explainable AI using Python artificial intelligence model explanations using Python-based libraries, extensions, and frameworks |
| title_full | Practical explainable AI using Python artificial intelligence model explanations using Python-based libraries, extensions, and frameworks Pradeepta Mishra |
| title_fullStr | Practical explainable AI using Python artificial intelligence model explanations using Python-based libraries, extensions, and frameworks Pradeepta Mishra |
| title_full_unstemmed | Practical explainable AI using Python artificial intelligence model explanations using Python-based libraries, extensions, and frameworks Pradeepta Mishra |
| title_short | Practical explainable AI using Python |
| title_sort | practical explainable ai using python artificial intelligence model explanations using python based libraries extensions and frameworks |
| title_sub | artificial intelligence model explanations using Python-based libraries, extensions, and frameworks |
| topic | Python (Computer program language) Artificial intelligence Data processing Application software Development Python (Langage de programmation) Intelligence artificielle ; Informatique Logiciels d'application ; Développement Application software ; Development Artificial intelligence ; Data processing |
| topic_facet | Python (Computer program language) Artificial intelligence Data processing Application software Development Python (Langage de programmation) Intelligence artificielle ; Informatique Logiciels d'application ; Développement Application software ; Development Artificial intelligence ; Data processing |
| work_keys_str_mv | AT mishrapradeepta practicalexplainableaiusingpythonartificialintelligencemodelexplanationsusingpythonbasedlibrariesextensionsandframeworks |