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Can I read Learn Python Generative AI: Journey from autoencoders to transformers to large language models on EtoBox?
Learn Python Generative AI: Journey from autoencoders to transformers to large language models by Zonunfeli Ralte, Indrajit Kar is a nonfiction available to read on EtoBox.
What is Learn Python Generative AI: Journey from autoencoders to transformers to large language models about?
Learn to unleash the power of AI creativity KEY FEATURES ● Understand the core concepts related to generative AI. ● Different types of generative models and their applications. ● Learn how to design generative AI neural networks using Python and TensorFlow. DESCRIPTION This book researches the intricate world of generative Artificial Intelligence, offering readers an extensive understanding of various components and applications in this field. The book begins with an in-depth analysis of generative models, providing a solid foundation and exploring their combination nuances. It then focuses on enhancing TransVAE, a variational autoencoder, and introduces the Swin Transformer in generative AI. The inclusion of cutting edge applications like building an image search using Pinecone and a vector database further enriches its content. The narrative shifts to practical applications, showcasing GenAI's impact in healthcare, retail, and finance, with real-world examples and innovative solutions. In the healthcare sector, it emphasizes AI's transformative role in diagnostics and patient care. In retail and finance, it illustrates how AI revolutionizes customer engagement and decision making
Who reads Learn Python Generative AI: Journey from autoencoders to transformers to large language models?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Zonunfeli Ralte, Indrajit Kar
- Publisher
- BPB Publications
- Published
- 2024
- Language
- EN
- ISBN
- 9789355518972
- Category
- nonfiction
- Subjects
- Computer Science, Programming, Stem
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