Generative AI Basics

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This practical guide demystifies generative AI, offering a clear introduction to its underlying concepts and methods. The book covers foundational topics, such as neural networks, deep learning, and key architectures like GPT, BERT, and GANs. Readers will gain hands-on experience by building and training AI models using open-source frameworks. Detailed examples and exercises guide users through building text generation, image synthesis, and more. Whether you’re a beginner or an AI enthusiast, this guide provides a solid foundation for understanding and implementing generative AI across various

2024/10/14

Generative AI Basics

It offers a comprehensive guide to generative AI, covering its fundamental principles, key algorithms, and practical applications. It explores the history and evolution of generative AI, introducing core concepts like neural networks, deep learning, and latent space. The text further examines prominent generative models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based models like GPT. The PDF provides step-by-step instructions on building generative models for image generation, text generation, and music composition, along with discussions on training and evaluation methods. Finally, it delves into ethical considerations in generative AI, emphasizing bias, fairness, and the potential for misuse. The text concludes with a look at the future of generative AI, highlighting emerging trends, the impact on society, and the importance of responsible development

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A comprehensive guide to Large Language Model (LLM) engineering, covering fundamental concepts, development practices, deployment strategies, and ethical considerations. The guide starts by introducing LLMs, their history, and various applications, then explores key NLP concepts and the Transformer architecture. The text then delves into LLM training techniques, including data collection, preprocessing, fine-tuning, and performance optimization. It also provides practical examples and hands-on exercises to illustrate various concepts and techniques.

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This practical guide demystifies generative AI, offering a clear introduction to its underlying concepts and methods. The book covers foundational topics, such as neural networks, deep learning, and key architectures like GPT, BERT, and GANs. Readers will gain hands-on experience by building and training AI models using open-source frameworks. Detailed examples and exercises guide users through building text generation, image synthesis, and more. Whether you’re a beginner or an AI enthusiast, this guide provides a solid foundation for understanding and implementing generative AI across various