Artículos relacionados a Explainable AI Recipes: Implement Solutions to Model...
Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python - Tapa blanda
Sinopsis
Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms.
The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution.
After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses.
What You Will Learn
- Create code snippets and explain machine learning models using Python
- Leverage deep learning models using the latest code with agile implementations
- Build, train, and explain neural network models designed to scale
- Understand the different variants of neural network models
Who This Book Is For
AI engineers, data scientists, and software developers interested in XAI
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Pradeepta Mishra is the Director of AI, Fosfor at L&T Infotech (LTI). He leads a large group of data scientists, computational linguistics experts, and machine learning and deep learning experts in building the next-generation product―Leni―which is the world’s first virtual data scientist. He has expertise across core branches of artificial intelligence, including autonomous ML and deep learning pipelines, ML ops, image processing, audio processing, natural language processing (NLP), natural language generation (NLG), design and implementation of expert systems, and personal digital assistants (PDAs). In 2019 and 2020, he was named one of "India's Top 40 Under 40 Data Scientists" by Analytics India magazine. Two of his books have been translated into Chinese and Spanish, based on popular demand.
Pradeepa delivered a keynote session at the Global Data Science Conference 2018, USA. He delivered a TEDx talk on "Can Machines Think?", available on the official TEDx YouTube channel. He has mentored more than 2,000 data scientists globally. He has delivered 200+ tech talks on data science, ML, DL, NLP, and AI at various universities, meetups, technical institutions, and community-arranged forums. He is a visiting faculty member to more than 10 universities, where he teaches deep learning and machine learning to professionals, and mentors them in pursuing a rewarding career in artificial intelligence.
De la contraportada
Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms.
The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution.
After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses.
You will:
- Create code snippets and explain machine learning models using Python
- Leverage deep learning models using the latest code with agile implementations
- Build, train, and explain neural network models designed to scale
- Understand the different variants of neural network models
"Sobre este título" puede pertenecer a otra edición de este libro.
- EditorialApress
- Año de publicación2023
- ISBN 10 1484290283
- ISBN 13 9781484290286
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de edición1
- Número de páginas280
- Contacto del fabricanteno disponible
EUR 28,31
EUR 17,48 gastos de envío desde Estados Unidos de America a España
Destinos, gastos y plazos de envíoComprar nuevo
Ver este artículo
EUR 27,19
GRATIS gastos de envío desde Estados Unidos de America a España
Destinos, gastos y plazos de envío
Resultados de la búsqueda para Explainable AI Recipes: Implement Solutions to Model...
Edición internacional
Explainable AI Recipes
Edición internacionalLibrería: Romtrade Corp., STERLING HEIGHTS, MI, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide. Nº de ref. del artículo: ABNR-209478
Cantidad disponible: 2 disponibles
Imagen de archivo
Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python
Librería: California Books, Miami, FL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: I-9781484290286
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python (Paperback or Softback)
Publicado por
Apress 2/9/2023, 2023
ISBN 10: 1484290283
ISBN 13: 9781484290286
Nuevo
Paperback or Softback
Librería: BargainBookStores, Grand Rapids, MI, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Paperback or Softback. Condición: New. Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python 0.87. Book. Nº de ref. del artículo: BBS-9781484290286
Cantidad disponible: 5 disponibles
Imagen de archivo
Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. In. Nº de ref. del artículo: ria9781484290286_new
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Explainable Ai Recipes : Implement Solutions to Model Explainability and Interpretability With Python
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 45290722-n
Cantidad disponible: 15 disponibles
Imagen de archivo
Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python
Impresión bajo demandaLibrería: THE SAINT BOOKSTORE, Southport, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 184. Nº de ref. del artículo: C9781484290286
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Explainable Ai Recipes : Implement Solutions to Model Explainability and Interpretability With Python
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 45290722
Cantidad disponible: 15 disponibles
Imagen del vendedor
Explainable AI Recipes
Impresión bajo demandaLibrería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms.The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution,and activation attribution. After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses.What You Will LearnCreate code snippets and explain machine learning models using PythonLeverage deep learning models using the latest code with agile implementationsBuild, train, and explain neural network models designed to scaleUnderstand the different variants of neural network modelsWho This Book Is ForAI engineers, data scientists, and software developers interested in XAI 280 pp. Englisch. Nº de ref. del artículo: 9781484290286
Cantidad disponible: 2 disponibles
Imagen del vendedor
Explainable AI Recipes : Implement Solutions to Model Explainability and Interpretability with Python
Impresión bajo demandaLibrería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms.The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution,and activation attribution. After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses.What You Will LearnCreate code snippets and explain machine learning models using PythonLeverage deep learning models using the latest code with agile implementationsBuild, train, and explain neural network models designed to scaleUnderstand the different variants of neural network modelsWho This Book Is ForAI engineers, data scientists, and software developers interested in XAI. Nº de ref. del artículo: 9781484290286
Cantidad disponible: 1 disponibles
Imagen de archivo
Explainable Ai Recipes: Implement Solutions to Model Explainability and Interpretability With Python
Librería: Revaluation Books, Exeter, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: Brand New. 278 pages. 9.25x6.10x0.59 inches. In Stock. Nº de ref. del artículo: x-1484290283
Cantidad disponible: 2 disponibles