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Generative AI Fundamentals: A Guide for Beginners
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- FormatePub
- ISBN8227709905
- EAN9798227709905
- Date de parution08/07/2024
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurBig Dog Books, LLC
Résumé
Generative AI is a groundbreaking development in the field of artificial intelligence. Unlike standard AI systems, which are designed to classify or forecast based on input data, generative AI systems have the unique capacity to generate new data. This includes creating lifelike photos, coherent writing, melodic music, and even intricate 3D models. Generative AI has numerous uses, including creative arts and entertainment, scientific research, and industrial design. The book is intended to serve as a thorough introduction to the intriguing and quickly expanding topic of generative artificial intelligence.
Whether you are a student, a professional, or simply an enthusiast interested in the potential of AI to create and innovate, this guide will equip you with the fundamental information and practical insights you need to comprehend and operate with generative AI. The book consists of Nine chapters. Chapter One begins with an overview of generative AI, studying its history, significance, and the numerous sectors where it has had a significant impact.
Chapter Two digs into the fundamentals of machine learning, offering the foundation required to understand more advanced topics. Chapter Three presents the framework for deep learning, which is an essential component of modern AI and fuels many generative models. Chapter Four investigates various forms of generative models, emphasizing their distinct qualities and uses. In Chapter Five, Variational Autoencoders (VAEs) are examined, as a powerful class of generative models known for their capacity to learn latent representations of data.
Chapter Six focuses on Generative Adversarial Networks (GANs), which have transformed the area of AI with their novel technique to creating realistic data. Chapter Seven focuses on Generative Models in Natural Language Processing, where these models can be used to generate and understand human language, thereby opening new possibilities in communication and automation. Chapter Eight gives practical guidance for developing and deploying generative AI solutions.
Finaly, chapter nine presents the evaluation of Generative Models that involves assessing their performance using qualitative techniques and quantitative metrics to ensure they produce high-quality, diverse, and realistic outputs. Robustness and generalization are also crucial factors, tested through methods like cross-validation and stress testing, to guarantee reliable performance on unseen data.
Thank you for selecting this guide as your beginning point in the field of generative AI. We are thrilled to accompany you on this adventure and can't wait to see what you will create.
Whether you are a student, a professional, or simply an enthusiast interested in the potential of AI to create and innovate, this guide will equip you with the fundamental information and practical insights you need to comprehend and operate with generative AI. The book consists of Nine chapters. Chapter One begins with an overview of generative AI, studying its history, significance, and the numerous sectors where it has had a significant impact.
Chapter Two digs into the fundamentals of machine learning, offering the foundation required to understand more advanced topics. Chapter Three presents the framework for deep learning, which is an essential component of modern AI and fuels many generative models. Chapter Four investigates various forms of generative models, emphasizing their distinct qualities and uses. In Chapter Five, Variational Autoencoders (VAEs) are examined, as a powerful class of generative models known for their capacity to learn latent representations of data.
Chapter Six focuses on Generative Adversarial Networks (GANs), which have transformed the area of AI with their novel technique to creating realistic data. Chapter Seven focuses on Generative Models in Natural Language Processing, where these models can be used to generate and understand human language, thereby opening new possibilities in communication and automation. Chapter Eight gives practical guidance for developing and deploying generative AI solutions.
Finaly, chapter nine presents the evaluation of Generative Models that involves assessing their performance using qualitative techniques and quantitative metrics to ensure they produce high-quality, diverse, and realistic outputs. Robustness and generalization are also crucial factors, tested through methods like cross-validation and stress testing, to guarantee reliable performance on unseen data.
Thank you for selecting this guide as your beginning point in the field of generative AI. We are thrilled to accompany you on this adventure and can't wait to see what you will create.
Generative AI is a groundbreaking development in the field of artificial intelligence. Unlike standard AI systems, which are designed to classify or forecast based on input data, generative AI systems have the unique capacity to generate new data. This includes creating lifelike photos, coherent writing, melodic music, and even intricate 3D models. Generative AI has numerous uses, including creative arts and entertainment, scientific research, and industrial design. The book is intended to serve as a thorough introduction to the intriguing and quickly expanding topic of generative artificial intelligence.
Whether you are a student, a professional, or simply an enthusiast interested in the potential of AI to create and innovate, this guide will equip you with the fundamental information and practical insights you need to comprehend and operate with generative AI. The book consists of Nine chapters. Chapter One begins with an overview of generative AI, studying its history, significance, and the numerous sectors where it has had a significant impact.
Chapter Two digs into the fundamentals of machine learning, offering the foundation required to understand more advanced topics. Chapter Three presents the framework for deep learning, which is an essential component of modern AI and fuels many generative models. Chapter Four investigates various forms of generative models, emphasizing their distinct qualities and uses. In Chapter Five, Variational Autoencoders (VAEs) are examined, as a powerful class of generative models known for their capacity to learn latent representations of data.
Chapter Six focuses on Generative Adversarial Networks (GANs), which have transformed the area of AI with their novel technique to creating realistic data. Chapter Seven focuses on Generative Models in Natural Language Processing, where these models can be used to generate and understand human language, thereby opening new possibilities in communication and automation. Chapter Eight gives practical guidance for developing and deploying generative AI solutions.
Finaly, chapter nine presents the evaluation of Generative Models that involves assessing their performance using qualitative techniques and quantitative metrics to ensure they produce high-quality, diverse, and realistic outputs. Robustness and generalization are also crucial factors, tested through methods like cross-validation and stress testing, to guarantee reliable performance on unseen data.
Thank you for selecting this guide as your beginning point in the field of generative AI. We are thrilled to accompany you on this adventure and can't wait to see what you will create.
Whether you are a student, a professional, or simply an enthusiast interested in the potential of AI to create and innovate, this guide will equip you with the fundamental information and practical insights you need to comprehend and operate with generative AI. The book consists of Nine chapters. Chapter One begins with an overview of generative AI, studying its history, significance, and the numerous sectors where it has had a significant impact.
Chapter Two digs into the fundamentals of machine learning, offering the foundation required to understand more advanced topics. Chapter Three presents the framework for deep learning, which is an essential component of modern AI and fuels many generative models. Chapter Four investigates various forms of generative models, emphasizing their distinct qualities and uses. In Chapter Five, Variational Autoencoders (VAEs) are examined, as a powerful class of generative models known for their capacity to learn latent representations of data.
Chapter Six focuses on Generative Adversarial Networks (GANs), which have transformed the area of AI with their novel technique to creating realistic data. Chapter Seven focuses on Generative Models in Natural Language Processing, where these models can be used to generate and understand human language, thereby opening new possibilities in communication and automation. Chapter Eight gives practical guidance for developing and deploying generative AI solutions.
Finaly, chapter nine presents the evaluation of Generative Models that involves assessing their performance using qualitative techniques and quantitative metrics to ensure they produce high-quality, diverse, and realistic outputs. Robustness and generalization are also crucial factors, tested through methods like cross-validation and stress testing, to guarantee reliable performance on unseen data.
Thank you for selecting this guide as your beginning point in the field of generative AI. We are thrilled to accompany you on this adventure and can't wait to see what you will create.
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