The book delves into the fascinating world of Deep Neural Networks (DNNs), a powerful tool within Artificial Intelligence (AI). It explores how DNNs learn and make predictions based on data.
Clarifying DNNs: The book explains the core concepts of DNNs, their structure, and how they extract patterns from training data.
Data preparation: Understanding the importance of various datasets, including training, testing, and unseen data, in building robust AI models.
Machine Learning and Deep Learning: The book provides a clear overview of Machine Learning (ML) and Deep Learning (DL) as foundational concepts for AI development.
Python libraries: Learn about Python libraries commonly used for AI and DNN implementation.
Designing AI Management Dashboards: Discover how to create dashboards to visualize and monitor AI performance.
Real-world applications of AI: Explore the diverse domains where AI is making a significant impact, including finance, and healthcare.
AI Engine integration: Understand the benefits of integrating AI engines with existing systems like ERP.
Generative AI: Learn about this exciting subfield of AI focused on creating new data.
50+ video tutorials: The book's website offers video tutorials demonstrating AI forecasting in various domains.
Overall, "AI-Modelling and Process" provides a valuable roadmap for understanding and implementing AI in today's data-driven world.
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Specifications
Book Details
Title
AI-Modelling and Process
Publication Year
2024 January
Product Form
Paperback
Publisher
Bluerose Publishers Pvt. Ltd.
ISBN13
9789359896021
Book Category
Biographies, Memoirs and General Non-Fiction Books
Book Subcategory
Other Books
Language
English
Contributors
Author Info
The book delves into the fascinating world of Deep Neural Networks (DNNs), a powerful tool within Artificial Intelligence (AI). It explores how DNNs learn and make predictions based on data.
Clarifying DNNs: The book explains the core concepts of DNNs, their structure, and how they extract patterns from training data.
Data preparation: Understanding the importance of various datasets, including training, testing, and unseen data, in building robust AI models.
Machine Learning and Deep Learning: The book provides a clear overview of Machine Learning (ML) and Deep Learning (DL) as foundational concepts for AI development.
Python libraries: Learn about Python libraries commonly used for AI and DNN implementation.
Designing AI Management Dashboards: Discover how to create dashboards to visualize and monitor AI performance.
Real-world applications of AI: Explore the diverse domains where AI is making a significant impact, including finance, and healthcare.
AI Engine integration: Understand the benefits of integrating AI engines with existing systems like ERP.
Generative AI: Learn about this exciting subfield of AI focused on creating new data.
50+ video tutorials: The book's website offers video tutorials demonstrating AI forecasting in various domains.
Overall, "AI-Modelling and Process" provides a valuable roadmap for understanding and implementing AI in today's data-driven world.