Applied Generative AI for Beginners (PDF)
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Applied Generative AI for Beginners is structured around detailed chapters that will guide you from foundational knowledge to practical implementation. It starts with an introduction to generative AI and its current landscape, followed by an exploration of how the evolution of neural networks led to the development of large language models. The book then delves into specific architectures like ChatGPT and Google Bard, offering hands-on demonstrations for implementation using tools like Sklearn. You'll also gain insight into the strategic aspects of implementing generative AI in an enterprise setting, with the authors covering crucial topics such as LLMOps, technology stack selection, and in-context learning. The latter part of the book explores generative AI for images and provides industry-specific use cases, making it a comprehensive guide for practical application in various domains.
Whether you're a data scientist looking to implement advanced models, a business leader aiming to leverage AI for enterprise growth, or an academic interested in cutting-edge advancements, this book offers a concise yet thorough guide to mastering generative AI, balancing theoretical knowledge with practical insights.
You will:
- Gain a solid understanding of generative AI, starting from the basics of neural networks and progressing to complex architectures like ChatGPT and Google Bard
- Implement large language models using Sklearn, complete with code examples and best practices for real-world application
- Learn how to integrate LLM's in enterprises, including aspects like LLMOps and technology stack selection
- Understand how generative AI can be applied across various industries, from healthcare and marketing to legal compliance through detailed use cases and actionable insights
Adarsha S is a data science and ML Ops leader. Presently, he is focused on creating world-class ML Ops capabilities to ensure continuous value delivery using AI. He aims to build a pool of exceptional data scientists within and outside the organization to solve problems through training programs, and always wants to stay ahead of the curve. He has worked in the pharma, healthcare, CPG, retail, and marketing industries. He lives in Bangalore and loves to read and teach data science.
Anoosh Kulkarni is a data scientist and ML Ops engineer. He has worked with various global enterprises across multiple domains solving their business problems using machine learning and AI. He has worked at Awok-dot-com, one of the leading e-commerce giants in UAE, where he focused on building state of art recommender systems and deep learning-based search engines. He is passionate about guiding and mentoring people in their data science journey. He often leads data sciences/machine learning meetups, helping aspiring data scientists carve their career road map.
Dilip Gudivada is a seasoned senior data architect with 13 years of experience in cloud services, big data, and data engineering. Dilip has a strong background in designing and developing ETL solutions, focusing specifically on building robust data lakes on the Azure cloud platform. Leveraging technologies such as Azure Databricks,
- Autoren: Akshay Kulkarni , Adarsha Shivananda , Anoosh Kulkarni , Dilip Gudivada
- 2023, 1st ed, 212 Seiten, Englisch
- Verlag: APress
- ISBN-10: 1484299949
- ISBN-13: 9781484299944
- Erscheinungsdatum: 21.11.2023
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Größe: 6.88 MB
- Ohne Kopierschutz
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