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Generative AI with Python and PyTorch: Navigating the AI frontier with LLMs, Stable Diffusion, and next-gen AI applications 2nd ed. Edition
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AZN 79
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Become an expert in Generative AI through immersive, hands-on projects that leverage today’s most powerful models for Natural Language Processing (NLP) and computer vision.
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Product Details
| Publisher | Packt Publishing |
| Publication date | March 28, 2025 |
| Edition | 2nd ed. |
| Language | English |
| Print length | 450 pages |
| ISBN-10 | 1835884458 |
| ISBN-13 | 978-1835884447 |
| Item Weight | 1.71 pounds (780 grams) |
| Dimensions | 7.5 x 1.03 x 9.25 inches (19.1 x 2.6 x 23.5 cm) |
Who Should Buy?
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AI Practitioners
Developers and researchers wanting to enhance their skills in generative AI using modern frameworks like PyTorch.
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Data Scientists
Professionals seeking to apply generative AI techniques to real-world problems and improve their project outcomes.
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Tech Educators
Instructors interested in teaching cutting-edge AI concepts and applications effectively through practical examples.
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Beginners
Individuals new to programming or AI concepts may find the material too advanced without prior knowledge.
Product Description
Generative AI with Python and PyTorch: Navigating the AI frontier with LLMs, Stable Diffusion, and next-gen AI applications 2nd ed. Edition
Customer Questions & Answers
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Question:
What is 'Generative AI with Python and PyTorch' about?
Answer: Generative AI with Python and PyTorch focuses on advanced techniques for developing AI applications using Python and the PyTorch framework. This edition covers essential topics like large language models (LLMs), Stable Diffusion for image generation, and innovative AI application development. This comprehensive guide is designed for both beginners and experienced programmers, providing practical examples and clear explanations that help readers to effectively navigate the rapidly evolving AI landscape. -
Question:
What topics are covered in the 2nd edition of this book?
Answer: The 2nd edition of this book covers a range of topics including LLMs, Stable Diffusion, neural networks, transfer learning, and best practices for AI application development. It also incorporates updates on current trends and tools in generative AI, making it relevant for today's tech environment. Whether you are interested in creating text-based applications or visual content, this edition offers insights and techniques necessary for practical implementation in real-world projects. -
Question:
Who is the target audience for this book?
Answer: This book is primarily targeted at software developers, data scientists, and machine learning enthusiasts who wish to deepen their understanding of generative AI techniques. Regardless of prior experience, readers will find valuable insights and practical projects that cater to varied skill levels. It is also an excellent resource for instructors looking to teach or incorporate generative AI topics into their curriculum. -
Question:
What programming skills do I need to understand this book?
Answer: To gain the most from 'Generative AI with Python and PyTorch', a basic understanding of Python programming is essential. Familiarity with machine learning concepts and libraries like PyTorch will further enhance your learning experience. The book gradually builds on concepts, making it accessible, even if you have minimal experience, as it explains foundational topics before advancing to complex applications. -
Question:
Can this book help me create my own AI applications?
Answer: Yes, this book provides the tools and knowledge necessary to create your own AI applications. It includes hands-on projects that guide you through the process of developing generative models using PyTorch. By the end of the book, you should be well-equipped to implement AI solutions in various sectors, including healthcare, finance, and the creative industries, showcasing your new skills in practical applications. -
Question:
What are LLMs and why are they important?
Answer: Large Language Models (LLMs) are a type of AI designed to comprehend and generate human-like text based on massive datasets. They play a crucial role in NLP (Natural Language Processing) tasks like text generation, translation, and conversation agents. Understanding LLMs is vital as they serve as the backbone for many modern AI applications, making it important for developers to know how to incorporate them effectively in their projects. -
Question:
What is Stable Diffusion and how is it used?
Answer: Stable Diffusion is a sophisticated generative model used for creating high-quality visual content. It leverages algorithms to produce stunning images based on textual descriptions or existing designs. The book elaborates on how to implement Stable Diffusion in projects, enabling creatives and developers to automate content generation, enhance artistic workflows, or even develop unique applications for advertising and digital mediums. -
Question:
Do I need any special hardware to work with the examples in this book?
Answer: While you can run many of the examples using standard hardware, having a GPU (Graphics Processing Unit) substantially speeds up the training and inference processes in generative models. For complex tasks, especially when dealing with high-resolution images or extensive datasets, it’s beneficial to utilize a modern machine equipped with a powerful GPU to enhance performance and efficiency. -
Question:
Where can I buy 'Generative AI with Python and PyTorch' in TN?
Answer: You can buy 'Generative AI with Python and PyTorch: Navigating the AI frontier with LLMs, Stable Diffusion, and next-gen AI applications 2nd ed.' from Ubuy. Ubuy offers a range of options including delivery services in Azerbaijan, making it convenient for you to get this essential resource for your AI journey.
Neural Networks Editorial Review
**** "Generative AI with Python and PyTorch: Navigating the AI Frontier with LLMs, Stable Diffusion, and Next-Gen AI Applications" (2nd edition) has received positive reception from readers, particularly those who are either new to the field of generative AI or looking to deepen their understanding of complex concepts. The book is praised for its comprehensive coverage of generative AI principles, providing a solid foundation for beginners while also serving as a valuable resource for more advanced readers. Reviewers appreciate the well-structured content which includes a mix of theoretical insights and practical applications, all supported by detailed diagrams that enhance understanding. Many readers highlight the accessibility of the material, making it approachable for novices who may feel overwhelmed by the topic. The inclusion of numerous online resources further assists learners in expanding their knowledge. Those with previous experience in related fields, such as GANs, find the book particularly enlightening, noting that it successfully encapsulates a historical perspective on generative AI's evolution, as well as current trends like large language models (LLMs) and Stable Diffusion. In summary, this book strikes a balance between being a primer for beginners and an informative guide for those seeking to expand their expertise in generative AI. It is generally regarded as a well-written and essential resource for anyone looking to navigate the evolving landscape of AI technologies. **
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Pros
- Comprehensive coverage of generative AI concepts.
- Suitable for both beginners and those with prior knowledge.
- Well-structured content with theoretical and practical balance.
- Detailed diagrams that aid understanding.
- Includes a wealth of online resources for further learning.
Cons
- Readers may not achieve expert-level knowledge solely from this book.
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AZN 79
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Features & Benefits
- Master GenAI techniques to create images and text.
- Implement real-world applications of LLMs and generative AI.
- Gain proficiency in fine-tuning models with PEFT and LoRA.
- Expand your skills with RAG techniques, LangChain, and LlamaIndex.
- Receive a free eBook with the purchase of print or Kindle.
- Designed for data scientists, ML engineers, and developers with Python experience.
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