Python for Generative AI: Practical Techniques, Applications, and Code Examples

Vemula, Anand

ISBN 13: 9798327023796

Edité par Independently published, 2024

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N° de réf. du vendeur 48043067-n

Détails bibliographiques

• Titre: Python for Generative AI: Practical Techniques, Applications, and Code Examples
• Éditeur: Independently published
• Date de parution: 2024
• Langue: anglais
• ISBN à 13 chiffres: 9798327023796
• Reliure: Couverture souple
• État de l'article: New

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Synopsis

"Python for Generative AI: Practical Techniques, Applications, and Code Examples" is a comprehensive guide that equips readers with the essential skills and knowledge to harness the power of Python in the exciting field of generative artificial intelligence (AI). From foundational concepts to advanced techniques, this book provides a hands-on approach to understanding and implementing generative AI models using Python.

The book begins with an introduction to the principles of generative AI, laying the groundwork for readers to grasp key concepts such as neural networks, generative models, and deep learning. Through clear explanations and practical examples, readers learn how to leverage Python libraries such as TensorFlow, PyTorch, and Keras to build and train various types of generative models.

Throughout the book, readers are guided through real-world applications and use cases of generative AI, including image generation and editing, text generation and natural language processing, music and audio synthesis, and video generation and editing. Each chapter is accompanied by code examples and demonstrations, allowing readers to follow along and implement the techniques discussed.

The book also covers advanced topics such as conditional generative models, StyleGAN and advanced GAN variants, enhancements and improvements in variational autoencoders (VAEs), and training and optimization techniques. Readers learn how to apply data augmentation techniques, perform hyperparameter tuning, debug and improve model performance, and evaluate generative models using qualitative and quantitative metrics.

In addition to technical skills, the book addresses ethical considerations, legal and regulatory aspects, and provides case studies and real-world projects showcasing the diverse applications of generative AI across industries. With its practical approach and emphasis on code examples, "Python for Generative AI: Practical Techniques, Applications, and Code Examples" serves as a valuable resource for students, researchers, and practitioners looking to explore and master the exciting field of generative AI using Python.