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Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps
Resolve the black box models in your AI applications to make them fair, trustworthy, and secure
Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps
Item #: 45674217

Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps

Item #: 45674217

KZT 41056

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Resolve the black box models in your AI applications to make them fair, trustworthy, and secure
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What Stands Out

Comprehensive Learning
Offers in-depth tutorials that cover the interpretation, visualization, and explanation of AI models, ensuring users grasp essential concepts and techniques for applying XAI in real-world scenarios.
Practical Application
Focuses on hands-on projects using Python, empowering learners to develop AI applications that are fair, secure, and trustworthy, making complex topics accessible through engaging, practical examples.
AI Integration
Teaches integration of explainable AI solutions into existing frameworks, helping developers enhance their AI applications' reliability and transparency, which is crucial for gaining user trust and implementing ethical AI practices.

Product Details

Develop fair, secure, and trustworthy AI applications with Python. Interpret, visualize, and explain AI models for reliable results. Get started now with Ubuy Kazakhstan.
  • Learn explainable AI tools and techniques to process trustworthy AI results
  • Understand how to detect, handle, and avoid common issues with AI ethics and bias
  • Integrate fair AI into popular apps and reporting tools to deliver business value using Python and associated tools
  • Work with hands-on machine learning Python projects strategically arranged to enhance understanding of AI results analysis
  • Become proficient in building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications using Python, TensorFlow 2, Google Cloud's XAI platform, Google Colaboratory, and other frameworks
  • Obtain in-depth knowledge of XAI concepts and learn to plan, estimate strengths and weaknesses, examine, review, share best practices, and integrate explainable AI results using Python models
Publisher Packt Publishing
Publication date July 31, 2020
Language English
Print length 454 pages
ISBN-10 1800208138
ISBN-13 978-1800208131
Item Weight 1.71 pounds (780 grams)
Dimensions 7.5 x 1.03 x 9.25 inches (19.1 x 2.6 x 23.5 cm)

Who Should Buy?

Suitable For
  • Data Scientists

    This product provides necessary tools for data scientists to enhance AI interpretability and model trustworthiness in applications.

  • AI Researchers

    Ideal for researchers exploring explainability in AI, enabling better insights and communication of complex algorithms.

  • Developers

    Developers building AI applications will benefit from integrating XAI principles to ensure fair and reliable models.

Not Suitable For
  • Beginners

    Users with no prior programming or machine learning experience may struggle with the book's concepts and implementations.

Product Description

Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps

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Intelligence & Semantics Editorial Review

Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps is an essential resource in the rapidly advancing field of machine learning and artificial intelligence. This book addresses the critical need for explainable AI, providing insights on techniques for effectively handling data, training models, and making predictions. Readers appreciate the current content covering tools like SHAP and LIME, along with a variety of datasets that offer fresh examples unlike those repeated in other texts. This is particularly useful for practitioners aiming to understand AI biases and ethical considerations crucial in today's technology landscape.

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Pros

  • Comprehensive guide to AI explainability techniques.
  • Includes practical examples with various datasets.
  • Focus on ethical considerations in AI development.
  • Covers open-source tools for Python practitioners.
  • Accessible language makes complex concepts easier to grasp.

Cons

  • Primarily focuses on Python programming only.

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