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Practical Generative AI for Data Science: From Theory to Real-World Applications
Paperback
Publisher: Independently Published
Published: Jun 20 2024
Pages: 148
Weight: 0.46
Height: 0.32 Width: 6.00 Depth: 9.00
Language: English
Practical Generative AI for Data Science: From Theory to Real-World Applications is a comprehensive guide that bridges the gap between theory and practical implementation of generative AI techniques in the field of data science. This book equips readers with essential knowledge and hands-on skills to effectively harness the power of generative models for diverse applications.
Starting with foundational concepts, the book introduces readers to various types of generative models, including Gaussian Mixture Models, Hidden Markov Models, Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, and more. Each model is explained with clear examples, use cases, and case studies drawn from industries such as finance, healthcare, and media.
The practical implementation section provides step-by-step tutorials and complete code solutions using popular frameworks like TensorFlow and PyTorch. Readers learn how to build and train models for tasks such as image generation, natural language processing (NLP), anomaly detection, and speech synthesis. Detailed explanations of model architectures, optimization techniques, and evaluation metrics ensure a deep understanding of each concept.
Furthermore, the book addresses advanced topics including conditional generative models, sequential generative models like RNNs and Transformers, energy-based models, and diffusion models. These chapters delve into cutting-edge research, emerging trends, and practical applications across various industries.
Ethical considerations and regulatory concerns associated with generative AI are also discussed, emphasizing the importance of fairness, transparency, and privacy in model development and deployment.
Practical Generative AI for Data Science is an indispensable resource for data scientists, machine learning engineers, and researchers looking to leverage generative AI for solving real-world problems. Whether you are new to generative models or seeking to deepen your expertise, this book provides the knowledge and tools needed to succeed in the rapidly evolving field of AI.
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