Generative Ai Build A Strong Foundation In Machine Learning A Step By Step Understanding Of Fundamental Concepts With Practical Applications
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Generative Ai: Build a Strong Foundation in Machine Learning (A Step-by-step Understanding of Fundamental Concepts With Practical Applications)
Are you curious about the potential of generative ai to impact your career and everyday life, but unsure where to start? Do complex ai platforms and ethical worries feel like barriers to unlocking this exciting technology? Many tech enthusiasts, students, and professionals face these very questions every day, but your path to demystifying ai begins here. Generative ai: beginners guide offers a step-by-step roadmap to understanding and applying ai for strategic advantage, ethical deployment, and practical integration in your life and work environments. Here's what's waiting inside: • Unlock the secrets of generative ai – find the answers you were looking for and debunk the myths that might be holding you back • A deep dive into top ai platforms such as chatgpt, gemini, claude, perplexity and bard – explained in a way anyone can understand • How to make money online with your own ai business – tips, tricks, and strategies • Explanation of the 'do's and don'ts' of ai • Comprehensive walkthroughs and prompts for using ai in real-life scenarios such as writing and editing. • Practical use-cases and real-life examples to inspire you • Overview of top online resources for learning and mastering generative ai Are you a forward-thinking business leader, striving to stay ahead in this fast-paced digital era? Are you intrigued by the unlimited potential of artificial intelligence but overwhelmed by its intricacies? Do ethical considerations and complexities of ai deployment leave you bewildered and hesitant?
GenAI on AWS
Author: Olivier Bergeret
language: en
Publisher: John Wiley & Sons
Release Date: 2025-03-19
The definitive guide to leveraging AWS for generative AI GenAI on AWS: A Practical Approach to Building Generative AI Applications on AWS is an essential guide for anyone looking to dive into the world of generative AI with the power of Amazon Web Services (AWS). Crafted by a team of experienced cloud and software engineers, this book offers a direct path to developing innovative AI applications. It lays down a hands-on roadmap filled with actionable strategies, enabling you to write secure, efficient, and reliable generative AI applications utilizing the latest AI capabilities on AWS. This comprehensive guide starts with the basics, making it accessible to both novices and seasoned professionals. You'll explore the history of artificial intelligence, understand the fundamentals of machine learning, and get acquainted with deep learning concepts. It also demonstrates how to harness AWS's extensive suite of generative AI tools effectively. Through practical examples and detailed explanations, the book empowers you to bring your generative AI projects to life on the AWS platform. In the book, you'll: Gain invaluable insights from practicing cloud and software engineers on developing cutting-edge generative AI applications using AWS Discover beginner-friendly introductions to AI and machine learning, coupled with advanced techniques for leveraging AWS's AI tools Learn from a resource that's ideal for a broad audience, from technical professionals like cloud engineers and software developers to non-technical business leaders looking to innovate with AI Whether you're a cloud engineer, software developer, business leader, or simply an AI enthusiast, Gen AI on AWS is your gateway to mastering generative AI development on AWS. Seize this opportunity for an enduring competitive advantage in the rapidly evolving field of AI. Embark on your journey to building practical, impactful AI applications by grabbing a copy today.
Generative AI Foundations in Python
Author: Carlos Rodriguez
language: en
Publisher: Packt Publishing Ltd
Release Date: 2024-07-26
Begin your generative AI journey with Python as you explore large language models, understand responsible generative AI practices, and apply your knowledge to real-world applications through guided tutorials Key Features Gain expertise in prompt engineering, LLM fine-tuning, and domain adaptation Use transformers-based LLMs and diffusion models to implement AI applications Discover strategies to optimize model performance, address ethical considerations, and build trust in AI systems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application. It is pivotal to understand the foundational concepts needed to implement generative AI. This guide explains the core concepts behind -of-the-art generative models by combining theory and hands-on application. Generative AI Foundations in Python begins by laying a foundational understanding, presenting the fundamentals of generative LLMs and their historical evolution, while also setting the stage for deeper exploration. You’ll also understand how to apply generative LLMs in real-world applications. The book cuts through the complexity and offers actionable guidance on deploying and fine-tuning pre-trained language models with Python. Later, you’ll delve into topics such as task-specific fine-tuning, domain adaptation, prompt engineering, quantitative evaluation, and responsible AI, focusing on how to effectively and responsibly use generative LLMs. By the end of this book, you’ll be well-versed in applying generative AI capabilities to real-world problems, confidently navigating its enormous potential ethically and responsibly.What you will learn Discover the fundamentals of GenAI and its foundations in NLP Dissect foundational generative architectures including GANs, transformers, and diffusion models Find out how to fine-tune LLMs for specific NLP tasks Understand transfer learning and fine-tuning to facilitate domain adaptation, including fields such as finance Explore prompt engineering, including in-context learning, templatization, and rationalization through chain-of-thought and RAG Implement responsible practices with generative LLMs to minimize bias, toxicity, and other harmful outputs Who this book is for This book is for developers, data scientists, and machine learning engineers embarking on projects driven by generative AI. A general understanding of machine learning and deep learning, as well as some proficiency with Python, is expected.