Ai Unraveled Master Gpt 4 Gemini Generative Ai Llms Prompt Engineering A Simplified Guide For Everyday Users

Dive into the revolutionary world of Artificial Intelligence with 'AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence'. This comprehensive guide is your portal to understanding AI's most intricate concepts and cutting-edge developments. Whether you're a curious beginner or an AI enthusiast, this book is tailored to unveil the complexities of AI in a simple, accessible manner. What's Inside: Fundamental AI Concepts: Journey through the basics of AI, machine learning, deep learning, and neural networks. AI in Action: Explore how AI is reshaping industries and society, diving into its applications in computer vision, natural language processing, and beyond. Ethical AI: Tackle critical issues like AI ethics and bias, understanding the moral implications of AI advancements. Industry Insights: Gain insights into how AI is revolutionizing industries and impacting our daily lives. The Future of AI: Forecast the exciting possibilities and challenges that lie ahead in the AI landscape. Special Focus on Generative AI & LLMs: Latest AI Trends: Stay updated with the latest in AI, including ChatGPT, Google Bard, GPT-4, Gemini, and more. Interactive Quizzes: Test your knowledge with engaging quizzes on Generative AI and Large Language Models (LLMs). Practical Guides: Master GPT-4 with a simplified guide, delve into advanced prompt engineering, and explore the nuances of temperature settings in AI. Real-World Applications: Learn how to leverage AI in various sectors, from healthcare to cybersecurity, and even explore its potential in areas like aging research and brain implants. For the AI Enthusiast: Prompt Engineering: Uncover secrets to crafting effective prompts for ChatGPT/Google Bard. AI Career Insights: Explore lucrative career paths in AI, including roles like AI Prompt Engineers. AI Investment Guide: Navigate the world of AI stocks and investment opportunities. Your Guide to Navigating AI: Do-It-Yourself Tutorials: From building custom ChatGPT applications to running LLMs locally, this book offers step-by-step guides. AI for Everyday Use: Learn how AI can assist in weight loss, social media, and more. 'AI Unraveled' is more than just a book; it's a resource for anyone looking to grasp the complexities of AI and its impact on our world. Get ready to embark on an enlightening journey into the realm of Artificial Intelligence!" More Topics Covered: Artificial Intelligence, Machine Learning, Deep Learning, NLP, AI Ethics, Robotics, Cognitive Computing, ChatGPT, OpenAI, Google Bard, Generative AI, LLMs, AI in Healthcare, AI Investments, and much more. GPT-4 vs Gemini: Pros and Cons Mastering GPT-4: Simplified Guide For everyday Users Advance Prompt Engineering Techniques: [Single Prompt Technique, Zero-Shot and Few-Shot, Zero-Shot and Few-Shot, Generated Knowledge Prompting, EmotionPrompt, Chain of Density (CoD), Chain of Thought (CoT), Validation of LLMs Responses, Chain of Verification (CoVe), Agents - The Frontier of Prompt Engineering, Prompt Chaining vs Agents, Tree of Thought (ToT), ReAct (Reasoning + Act), ReWOO (Reasoning WithOut Observation), Reflexion and Self-Reflection, Guardrails, RAIL (Reliable AI Markup Language), Guardrails AI, NeMo Guardrails] Understanding Temperature in GPT-4: A Guide to AI Probability and Creativity Retrieval-Augmented Generation (RAG) model in the context of Large Language Models (LLMs) like GPT-4 Prompt Ideas for ChatGPT/Google Bard How to Run ChatGPT-like LLMs Locally on Your Computer in 3 Easy Steps ChatGPT Custom Instructions Settings for Power Users Examples of bad and good ChatGPT prompts Top 5 Beginner Mistakes in Prompt Engineering Use ChatGPT like a PRO Prompt template for learning any skill Prompt Engineering for ChatGPT The Future of LLMs in Search What is Explainable AI? Which industries are meant for XAI? ChatGPT Best Tips, Cheat Sheet LLMs Utilize Vector DB for Data Storage The Limitation Technique in Prompt Responses Use ChatGPT to learn new subjects Prompts to proofread anything Topics: Artificial Intelligence Education Machine Learning Deep Learning Reinforcement Learning Neural networks Data science AI ethics Deepmind Robotics Natural language processing Intelligent agents Cognitive computing AI Apps AI impact AI Tech ChatGPT Open AI Safe AI Generative AI Discriminative AI Sam Altman Google Bard NVDIA Large Language Models (LLMs) PALM GPT Explainable AI GPUs AI Stocks AI Podcast Q* AI Certification AI Quiz RAG How to access the AI Unraveled print and audiobook: Amazon print book: https://amzn.to/3xvCfWR Audible at Amazon : https://www.audible.com/pd/B0BXMJ7FK5/?source_code=AUDFPWS0223189MWT-BK-ACX0-343437&ref=acx_bty_BK_ACX0_343437_rh_us (Use Promo code: 37YT3B5UYUYZW) Audiobook at Google: https://play.google.com/store/audiobooks/details?id=AQAAAEAihFTEZM Amazon eBook: https://amzn.to/3KbshkO Google eBook: https://play.google.com/store/books/details?id=oySuEAAAQBAJ Apple eBook: http://books.apple.com/us/book/id6445730691

Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. Learn how to empower AI to work for you. This book explains: The structure of the interaction chain of your program's AI model and the fine-grained steps in between How AI model requests arise from transforming the application problem into a document completion problem in the model training domain The influence of LLM and diffusion model architecture—and how to best interact with it How these principles apply in practice in the domains of natural language processing, text and image generation, and code

Unlock the secrets of effective communication with AI through "AI Foundations of Prompt Engineering," a comprehensive guide that delves into the nuanced art of prompt engineering. Authored with the intent to demystify the process of crafting prompts that elicit desired responses from AI, this book serves as both an introduction for newcomers and a deep dive for seasoned professionals in the field of artificial intelligence. Book Contents: Chapter 1: Defining Prompt Engineering - Introduction to the concept and importance of prompt engineering in AI interactions. Chapter 2: Elements of a Good Prompt - Exploration of the critical components that constitute an effective prompt. Chapter 3: Word Choice in Prompts - Insights into the significance of choosing the right words for clarity and impact. Chapter 4: Crafting Questions for AI - Guidelines for formulating questions that AI can understand and respond to accurately. Chapter 5: Sentence Structure in AI Prompts - Examination of how sentence structure affects AI's comprehension and responses. Chapter 6: Avoiding Ambiguity - Strategies for creating clear and unambiguous prompts to ensure precise AI responses. Chapter 7: Contextual Considerations - Discussion on the role of context in enhancing the effectiveness of prompts. Chapter 8: Testing and Iterating Prompts - Methods for testing prompts and refining them based on feedback. Chapter 9: Advanced Prompt Engineering - Advanced techniques and considerations for expert-level prompt engineering. Why This Book? Practical Approach: Unlike purely theoretical texts, this book offers concrete guidance, practical examples, and exercises for real-world application. Comprehensive Coverage: From foundational concepts to advanced techniques, this book covers the entire spectrum of prompt engineering. Expert Insights: Benefit from the author's deep understanding of AI dialogue design, informed by the latest research and practices in the field. Whether you're just starting out or seeking to enhance your skills in AI communication, "AI Foundations of Prompt Engineering" is an essential resource that will equip you with the knowledge and tools to master the art of prompt engineering. Embark on this educational journey and unlock the potential of AI through expertly crafted prompts.

This book is designed for mastering the art and science of prompt engineering in the realm of artificial intelligence. It provides a thorough exploration of the fundamental principles, practical techniques, and real-world applications of prompt engineering, with a special focus on ChatGPT and its advanced iterations, including GPT-4 and GPT plug-ins. You will learn the essential role of prompts in interacting with ChatGPT, understand the anatomy of a well-constructed prompt, and explore different prompt styles. The book also shows how to set constraints and guidelines to steer AI responses toward desired outcomes and ensure ethical and responsible interactions. Whether you're a novice of generative AI or seeking advanced insights, this book is your companion for mastering prompt engineering and building innovative GPT applications. FEATURES: Discusses the fundamental groundwork for understanding prompts, their role in guiding AI models, and their impact on the quality of generated responses Provides real-world applications demonstrating how prompt engineering is applied across various domains, from customer support and education to business and technical fields Includes a chapter on GPT-4 Plugins showing how to extend the capabilities of GPT-4 and create more versatile AI-powered applications Describes techniques for formulating clear and direct questions to obtain precise answers from AI models

Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. Learn how to empower AI to work for you. This book explains: The structure of the interaction chain of your program's AI model and the fine-grained steps in between How AI model requests arise from transforming the application problem into a document completion problem in the model training domain The influence of LLM and diffusion model architecture--and how to best interact with it How these principles apply in practice in the domains of natural language processing, text and image generation, and code

Let’s go beyond simple use cases and ‘try this prompt’. ChatGPT is an incredible AI tool that has the potential to revolutionise the way we work. Bill Gates recently stated that ChatGPT is as big an invention as the internet, and that it could essentially change the world. However, to be able to get the most out of ChatGPT, and move beyond simple use cases, you will need to learn the art of prompt engineering. Check out a free sample chapter: www.ChatGPTtrainings.com Why This Book? This book is designed to help you learn the art of working with ChatGPT in such a way that you get much better. This skill, prompt engineering, is what separates good from great when it comes to using ChatGPT. - Learn 4 core techniques and tools to write better prompts - Learn 2 advanced prompt engineering tools to take your knowledge to the next level - Discover how GPT-4 is a game-changer and become an early adopter - Explore 9 in-depth use cases to understand how to apply these techniques and tools - Multiple exercises for each technique and tool to ensure you learn by doing - Beautiful illustrations throughout the book - Accessible language in a personal tone - Written by a senior trainer as a narrated instruction manual and guide

Understanding and coding advanced deep learning algorithms with the most intuitive deep learning library in existence Key Features Explore the most advanced deep learning techniques that drive modern AI results Implement deep neural networks, autoencoders, GANs, VAEs, and deep reinforcement learning A wide study of GANs, including Improved GANs, Cross-Domain GANs, and Disentangled Representation GANs Book DescriptionRecent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You then learn all about GANs, and how they can open new levels of AI performance. Next, you’ll get up to speed with how VAEs are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.What you will learn Cutting-edge techniques in human-like AI performance Implement advanced deep learning models using Keras The building blocks for advanced techniques - MLPs, CNNs, and RNNs Deep neural networks – ResNet and DenseNet Autoencoders and Variational Autoencoders (VAEs) Generative Adversarial Networks (GANs) and creative AI techniques Disentangled Representation GANs, and Cross-Domain GANs Deep reinforcement learning methods and implementation Produce industry-standard applications using OpenAI Gym Deep Q-Learning and Policy Gradient Methods Who this book is for Some fluency with Python is assumed. As an advanced book, you'll be familiar with some machine learning approaches, and some practical experience with DL will be helpful. Knowledge of Keras or TensorFlow 1.x is not required but would be helpful.

Internet of Things (IoT) is one of the most hyped concept in today's technology world. However, with so much hype, still there is a lot of confusion on what does Internet of Things actually mean and what it takes to build IoT applications and how to apply it in various industries. There exist many definitions on "Internet of Things" and even getting on to a same terminology and definition seems difficult nowadays. We have seen multiple definitions over a period of time, such as 'Internet of Everything', 'Internet of your Things', 'Internet of People' and the list goes on. They all mean the same, so let's start with a simple definition of Internet of Things. "Internet of Things is a vision where every object in the world has the potential to connect to the Internet and provide their data so as to derive actionable insights on its own or through other connected objects" The object can be anything - a vehicle, machinery, airport, city, people, phone or even a shoe. From a connected vehicle solution, you can understand the driver behaviour and vehicle usage patterns, from a connected machines solution you can determine when do machines need servicing, from a connected airport solution you can understand many things like - how much time the passenger needs to wait for check-in and security, from an operating perspective it could help to optimize the passenger movement and ensure the right equipments are available at the right time to ensure quick serviceability and finally say, from a connected footwear solution you can understand how much you have run so far and your app can automatically purchase a new pair of shoes based on the remaining shoe life. As we can see, it's not just about connectivity, but how to use the connected data in context of your application or for that matter other connected solutions to derive insights which can't be uncovered before. Today we are seeing data (both structured and unstructured) growing by leaps and bounds available through mediums like blogs, social media, transactional systems etc. With advent of IoT, you will see a large volume of raw data emitting from devices like sensors. Such huge and complex set of data, if not attended to, can go wasted and opportunity lost in terms of building smart environment around us. While focusing on issue of addressing this web of complexity, often understanding the real benefit of IoT is lost and most importantly how to get started on IoT. In this book, our focus will be to provide a clear vision on IoT and everything you should know to get started on applying and building Enterprise IoT applications in any industry. The concepts listed down in the book are applicable across industries. Till date, it's difficult to find a single perspective of what does an Enterprise IoT stack actually mean and our intent is to provide an applicability guide that can be taken as reference for building any IoT application. In the course of the book, we would describe some of the key components of Internet of Things through our Enterprise IoT stack. We would look at how to incrementally apply IoT transformations to build connected products in various industries. At the end, we would understand the technical strategy and how to build IoT applications using IoT cloud offerings from Microsoft, IBM, and Amazon and even build one using open source technologies. To summarize, as part of the book we would cover the following - * A detailed overview of key components of Internet of Things and most comprehensive view of an Enterprise IoT stack. * How to apply IoT in context of real world applications by covering detailed use cases on manufacturing, automotive and home automation. * Understand the technical strategy and how to implement IoT applications using Microsoft Azure and IoT Suite, IBM Internet of Things Foundation and BlueMix, Amazon IoT and AWS services and various open source technologies like Apache Kakfa, Apache Spark and map it to our Enterprise IoT Stack.