Price: $37.95
(as of Sep 14, 2024 05:12:02 UTC – Details)
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. The book concludes by synthesizing key learnings, offering insights into the future of generative AI, and making it a comprehensive guide for diverse industries.
Readers will find themselves equipped with a profound understanding of generative AI, its current applications, and its boundless potential for future innovations.
What you will learn
● Acquire practical skills in designing and implementing various generative AI models.
● Gain expertise in vector databases and image embeddings, crucial for image search and data retrieval.
● Navigate challenges in healthcare, retail, and finance using sector specific insights.
● Generate images and text with VAEs, GANs, LLMs, and vector databases.
● Focus on both traditional and cutting edge techniques in generative AI.
Who this book is for
This book is for current and aspiring emerging AI deep learning professionals, architects, students, and anyone who is starting and learning a rewarding career in generative AI.
Table of Contents
1. Introducing Generative AI
2. Designing Generative Adversarial Networks
3. Training and Developing Generative Adversarial Networks
4. Architecting Auto Encoder for Generative AI
5. Building and Training Generative Autoencoders
6. Designing Generative Variation Auto Encoder
7. Building Variational Autoencoders for Generative AI
8. Fundamental of Designing New Age Generative Vision Transformer
9. Implementing Generative Vision Transformer
10. Architectural Refactoring for Generative Modeling
11. Major Technical Roadblocks in Generative AI and Way Forward
12. Overview and Application of Generative AI Models
13. Key Learnings
From the Publisher
Can you discuss the practical applications of generative AI in healthcare, retail, and finance from your book and their real-world impact?
The examples provided in the book are realistic representations of what is currently unfolding in the industry. In fact, some of the use cases mentioned are already in progress.
Generative AI is making a significant impact across various sectors such as healthcare, retail, and finance due to its ability to analyze large datasets, automate tasks, and generate new content, insights, or predictions.
What motivated you to write “Learn Python Generative AI,” and how do you envision it standing out in the rapidly evolving field of AI literature?
We aim to present a perspective on Generative AI, transitioning from conventional methods to modern approaches.
Additionally, we seek to offer insights from both research and industry perspectives.
Our goal is also to empower learners with practical skills in Python for Generative AI applications. This is the main motivation
How accessible is the advanced content on SWIN-Transformer and encoder-decoder architectures for readers without a deep AI background?
The accessibility of advanced topics like the SWIN-Transformer and the integration of encoder-decoder architectures with transformer models to readers without a deep background in AI depends largely on how the content is presented in the book.
These topics are complex and involve a significant amount of technical detail, typically requiring a foundational understanding of machine learning, neural networks, and possibly previous knowledge of basic transformer architectures.
Below are a few factors that can enhance accessibility
Publisher : BPB Publications (February 1, 2024)
Language : English
Paperback : 348 pages
ISBN-10 : 9355518978
ISBN-13 : 978-9355518972
Item Weight : 1.33 pounds
Dimensions : 7.5 x 0.79 x 9.25 inches
