McConnaughy, Tim

McNutt, Steve

Miles, Christopher

A pragmatic guide to acing the AWS ANS-C01 exam

AWS Certified Advanced Networking – Specialty (ANS-C01) Certification Guide

Despoudis, Theofanis

Build clean and scalable apps with proven patterns and expert insights

TypeScript 5 Design Patterns and Best Practices

Malavasi, Alexandre

A project-based guide to building web applications with ASP.NET Core 9 and JavaScript frameworks

Modern Full-Stack Web Development with ASP.NET Core

Verma, Jatin

Build a solid foundation in Snowflake to pass the SnowPro Core exam on the first attempt

SnowPro Core Certification Study Guide

Mukherjee, Amit

Casalaina, Marco

Saladi, Adithya

A practical guide to unlocking generative AI-powered innovation with Azure OpenAI

Azure OpenAI Essentials

Loredo, Robert

Write your own practical quantum programs with Python

Learn Quantum Computing with Python and IBM Quantum

Najlaa, Najihah

Master video editing with expert tips, techniques, and workflows

The Complete Guide to Adobe Premiere Pro 2025

Makhija, Jagjeet Singh

Odunukwe, Charles

Boost your skills with expert insights and certification-ready strategies for Microsoft analytics

Implementing Analytics Solutions Using Microsoft Fabric—DP-600 Exam Study Guide

Belmansour, Tidjani

Brady, Damian

A step-by-step guide to developing cross-platform CLI apps—from coding and testing to deployment

Building CLI Applications with C# and .NET

Apperson, Lem

Weinberger, Jonathan

Leverage C# scripting in Unity to create immersive games and VR experiences

Unity 6 Game Development with C# Scripting

Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more

Applied Machine Learning Explainability Techniques

<span xml:lang="en">Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more</span>

Bhattacharya, Aditya

Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systemsKey FeaturesExplore various explainability methods for designing robust and scalable explainable ML systemsUse XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problemsDesign user-centric explainable ML systems using guidelines provided for industrial applicationsBook DescriptionExplainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users.

Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML...

Applied Machine Learning Explainability Techniques: Make ML...

Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more

Format

Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more

Aditya Bhattacharya

Aditya Bhattacharya

La gente legge anche