The Prompt Engineers Handbook Effective Prompts For Optimal Results
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| Author | : Eslam Wahba |
| Publisher | : Independently Published |
| Total Pages | : 0 |
| Release | : 2024-07-20 |
| Genre | : Computers |
| ISBN | : |
Book Title: Handbook for Prompt Engineering: A Complete Guide and Examples for Prompt Engineering Science from Scratch to Application Unlock the full potential of AI-driven interactions with the definitive guide to prompt engineering. "Handbook for Prompt Engineering: A Complete Guide and Examples for Prompt Engineering Science from Scratch to Application" provides an authoritative and comprehensive exploration of the principles, techniques, and best practices essential for mastering prompt engineering in natural language processing (NLP) and artificial intelligence (AI). About the Book: Dive deep into the science of prompt engineering, starting with foundational concepts and progressing to advanced methodologies and practical applications. This handbook is meticulously structured into 35 chapters, each offering in-depth coverage of critical topics including designing effective prompts, evaluating and validating prompts, leveraging advanced customization techniques, and addressing ethical considerations in AI. **Key Features: ** 1. **Foundational Knowledge: ** - Understand the basics of prompt engineering and its significance in AI. - Learn about various types of prompts and their specific applications across different domains. 2. **Advanced Techniques: ** - Explore advanced prompt engineering methodologies such as prompt chaining, tuning, and utilizing auxiliary data. - Integrate cutting-edge technologies like quantum computing, federated learning, and edge AI to enhance AI performance. 3. **Ethical and Responsible AI: ** - Address ethical considerations, including bias mitigation, privacy protection, inclusivity, and transparency. - Implement strategies to ensure AI systems operate fairly and responsibly. 4. **Practical Applications: ** - Gain insights from detailed case studies that showcase real-world implementations of prompt engineering. - Learn how to build robust and scalable prompt-based models for healthcare, financial services, education, and more. 5. **Community Building and Collaboration: ** - Discover strategies for building and nurturing a community around prompt engineering. - Encourage knowledge sharing and collaboration to drive innovation and support the growth of the field. 6. **Comprehensive Resources: ** - Utilize appendices that include a glossary of key terms, sample prompts and use cases, essential tools and resources, and ethical guidelines and frameworks. Who Should Read This Book: - AI practitioners and researchers looking to deepen their expertise in prompt engineering. - Data scientists and machine learning engineers seeking to enhance their AI models. - Developers and NLP enthusiasts aiming to create more effective and interactive AI systems. - Ethicists and policymakers focused on promoting fair and responsible AI practices. Equip yourself with the knowledge and tools needed to excel in the rapidly evolving field of prompt engineering. Whether you are a seasoned AI professional or a newcomer to the field, this handbook provides the insights and practical guidance necessary to advance your skills and achieve success in AI-driven interactions. Purchase your copy of "Handbook for Prompt Engineering: A Complete Guide and Examples for Prompt Engineering Science from Scratch to Application" today and transform the way you design and implement AI prompts. Prompt engineering, NLP, AI, machine learning, data science, AI ethics, federated learning, quantum computing, AI models, human-AI collaboration, explainable AI, adaptive learning, zero-shot learning, few-shot learning, ethical AI, inclusive AI.
| Author | : Joyce L. Epstein |
| Publisher | : Corwin Press |
| Total Pages | : 508 |
| Release | : 2018-07-19 |
| Genre | : Education |
| ISBN | : 1483320014 |
Strengthen programs of family and community engagement to promote equity and increase student success! When schools, families, and communities collaborate and share responsibility for students′ education, more students succeed in school. Based on 30 years of research and fieldwork, the fourth edition of the bestseller School, Family, and Community Partnerships: Your Handbook for Action, presents tools and guidelines to help develop more effective and more equitable programs of family and community engagement. Written by a team of well-known experts, it provides a theory and framework of six types of involvement for action; up-to-date research on school, family, and community collaboration; and new materials for professional development and on-going technical assistance. Readers also will find: Examples of best practices on the six types of involvement from preschools, and elementary, middle, and high schools Checklists, templates, and evaluations to plan goal-linked partnership programs and assess progress CD-ROM with slides and notes for two presentations: A new awareness session to orient colleagues on the major components of a research-based partnership program, and a full One-Day Team Training Workshop to prepare school teams to develop their partnership programs. As a foundational text, this handbook demonstrates a proven approach to implement and sustain inclusive, goal-linked programs of partnership. It shows how a good partnership program is an essential component of good school organization and school improvement for student success. This book will help every district and all schools strengthen and continually improve their programs of family and community engagement.
| Author | : Phillip C. Wankat |
| Publisher | : Purdue University Press |
| Total Pages | : 494 |
| Release | : 2015-01-15 |
| Genre | : Education |
| ISBN | : 1612493629 |
The majority of professors have never had a formal course in education, and the most common method for learning how to teach is on-the-job training. This represents a challenge for disciplines with ever more complex subject matter, and a lost opportunity when new active learning approaches to education are yielding dramatic improvements in student learning and retention. This book aims to cover all aspects of teaching engineering and other technical subjects. It presents both practical matters and educational theories in a format useful for both new and experienced teachers. It is organized to start with specific, practical teaching applications and then leads to psychological and educational theories. The "practical orientation" section explains how to develop objectives and then use them to enhance student learning, and the "theoretical orientation" section discusses the theoretical basis for learning/teaching and its impact on students. Written mainly for PhD students and professors in all areas of engineering, the book may be used as a text for graduate-level classes and professional workshops or by professionals who wish to read it on their own. Although the focus is engineering education, most of this book will be useful to teachers in other disciplines. Teaching is a complex human activity, so it is impossible to develop a formula that guarantees it will be excellent. However, the methods in this book will help all professors become good teachers while spending less time preparing for the classroom. This is a new edition of the well-received volume published by McGraw-Hill in 1993. It includes an entirely revised section on the Accreditation Board for Engineering and Technology (ABET) and new sections on the characteristics of great teachers, different active learning methods, the application of technology in the classroom (from clickers to intelligent tutorial systems), and how people learn.
| Author | : Suzanne Lane |
| Publisher | : Routledge |
| Total Pages | : 676 |
| Release | : 2015-10-08 |
| Genre | : Education |
| ISBN | : 1136242570 |
The second edition of the Handbook of Test Development provides graduate students and professionals with an up-to-date, research-oriented guide to the latest developments in the field. Including thirty-two chapters by well-known scholars and practitioners, it is divided into five sections, covering the foundations of test development, content definition, item development, test design and form assembly, and the processes of test administration, documentation, and evaluation. Keenly aware of developments in the field since the publication of the first edition, including changes in technology, the evolution of psychometric theory, and the increased demands for effective tests via educational policy, the editors of this edition include new chapters on assessing noncognitive skills, measuring growth and learning progressions, automated item generation and test assembly, and computerized scoring of constructed responses. The volume also includes expanded coverage of performance testing, validity, fairness, and numerous other topics. Edited by Suzanne Lane, Mark R. Raymond, and Thomas M. Haladyna, The Handbook of Test Development, 2nd edition, is based on the revised Standards for Educational and Psychological Testing, and is appropriate for graduate courses and seminars that deal with test development and usage, professional testing services and credentialing agencies, state and local boards of education, and academic libraries serving these groups.
| Author | : Alex Hidalgo |
| Publisher | : O'Reilly Media |
| Total Pages | : 404 |
| Release | : 2020-08-05 |
| Genre | : Computers |
| ISBN | : 1492076783 |
Although service-level objectives (SLOs) continue to grow in importance, there’s a distinct lack of information about how to implement them. Practical advice that does exist usually assumes that your team already has the infrastructure, tooling, and culture in place. In this book, recognized SLO expert Alex Hidalgo explains how to build an SLO culture from the ground up. Ideal as a primer and daily reference for anyone creating both the culture and tooling necessary for SLO-based approaches to reliability, this guide provides detailed analysis of advanced SLO and service-level indicator (SLI) techniques. Armed with mathematical models and statistical knowledge to help you get the most out of an SLO-based approach, you’ll learn how to build systems capable of measuring meaningful SLIs with buy-in across all departments of your organization. Define SLIs that meaningfully measure the reliability of a service from a user’s perspective Choose appropriate SLO targets, including how to perform statistical and probabilistic analysis Use error budgets to help your team have better discussions and make better data-driven decisions Build supportive tooling and resources required for an SLO-based approach Use SLO data to present meaningful reports to leadership and your users
| Author | : John Bransford |
| Publisher | : W H Freeman & Company |
| Total Pages | : 262 |
| Release | : 1993 |
| Genre | : Psychology |
| ISBN | : 9780716722052 |
Provocative, challenging, and fun, The Ideal Problem Solver offers a sound, methodical approach for resolving problems based on the IDEAL (Identify, Define, Explore, Act, Look) model. The authors suggest new strategies for enhancing creativity, improving memory, criticizing ideas and generating alternatives, and communicating more effectively with a wider range of people. Using the results of laboratory research previously available only in a piece-meal fashion or in scientific journals, Bransford and Stein discuss such issues as Teaming new information, overcoming blocks to creativity, and viewing problems from a variety of perspectives.
| Author | : Nazia Habib |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 200 |
| Release | : 2019-04-19 |
| Genre | : Mathematics |
| ISBN | : 1789345758 |
Leverage the power of reward-based training for your deep learning models with Python Key FeaturesUnderstand Q-learning algorithms to train neural networks using Markov Decision Process (MDP)Study practical deep reinforcement learning using Q-NetworksExplore state-based unsupervised learning for machine learning modelsBook Description Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you get familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into modelfree Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym’s CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in real-world applications such as scientific research. Toward the end, you’ll gain a sense of what’s in store for reinforcement learning. By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow. What you will learnExplore the fundamentals of reinforcement learning and the state-action-reward processUnderstand Markov decision processesGet well versed with libraries such as Keras, and TensorFlowCreate and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI GymChoose and optimize a Q-Network’s learning parameters and fine-tune its performanceDiscover real-world applications and use cases of Q-learningWho this book is for If you are a machine learning developer, engineer, or professional who wants to delve into the deep learning approach for a complex environment, then this is the book for you. Proficiency in Python programming and basic understanding of decision-making in reinforcement learning is assumed.
| Author | : Susan A. Ambrose |
| Publisher | : John Wiley & Sons |
| Total Pages | : 336 |
| Release | : 2010-04-16 |
| Genre | : Education |
| ISBN | : 0470617608 |
Praise for How Learning Works "How Learning Works is the perfect title for this excellent book. Drawing upon new research in psychology, education, and cognitive science, the authors have demystified a complex topic into clear explanations of seven powerful learning principles. Full of great ideas and practical suggestions, all based on solid research evidence, this book is essential reading for instructors at all levels who wish to improve their students' learning." —Barbara Gross Davis, assistant vice chancellor for educational development, University of California, Berkeley, and author, Tools for Teaching "This book is a must-read for every instructor, new or experienced. Although I have been teaching for almost thirty years, as I read this book I found myself resonating with many of its ideas, and I discovered new ways of thinking about teaching." —Eugenia T. Paulus, professor of chemistry, North Hennepin Community College, and 2008 U.S. Community Colleges Professor of the Year from The Carnegie Foundation for the Advancement of Teaching and the Council for Advancement and Support of Education "Thank you Carnegie Mellon for making accessible what has previously been inaccessible to those of us who are not learning scientists. Your focus on the essence of learning combined with concrete examples of the daily challenges of teaching and clear tactical strategies for faculty to consider is a welcome work. I will recommend this book to all my colleagues." —Catherine M. Casserly, senior partner, The Carnegie Foundation for the Advancement of Teaching "As you read about each of the seven basic learning principles in this book, you will find advice that is grounded in learning theory, based on research evidence, relevant to college teaching, and easy to understand. The authors have extensive knowledge and experience in applying the science of learning to college teaching, and they graciously share it with you in this organized and readable book." —From the Foreword by Richard E. Mayer, professor of psychology, University of California, Santa Barbara; coauthor, e-Learning and the Science of Instruction; and author, Multimedia Learning
| Author | : Sudharsan Ravichandiran |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 484 |
| Release | : 2019-04-18 |
| Genre | : Computers |
| ISBN | : 1838640142 |
Apply modern reinforcement learning and deep reinforcement learning methods using Python and its powerful libraries Key FeaturesYour entry point into the world of artificial intelligence using the power of PythonAn example-rich guide to master various RL and DRL algorithmsExplore the power of modern Python libraries to gain confidence in building self-trained applicationsBook Description Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. This Learning Path will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The Learning Path starts with an introduction to RL followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. You'll also work on various datasets including image, text, and video. This example-rich guide will introduce you to deep RL algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore TensorFlow and OpenAI Gym to implement algorithms that also predict stock prices, generate natural language, and even build other neural networks. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many of the recent advancements in RL. By the end of the Learning Path, you will have all the knowledge and experience needed to implement RL and deep RL in your projects, and you enter the world of artificial intelligence to solve various real-life problems. This Learning Path includes content from the following Packt products: Hands-On Reinforcement Learning with Python by Sudharsan RavichandiranPython Reinforcement Learning Projects by Sean Saito, Yang Wenzhuo, and Rajalingappaa ShanmugamaniWhat you will learnTrain an agent to walk using OpenAI Gym and TensorFlowSolve multi-armed-bandit problems using various algorithmsBuild intelligent agents using the DRQN algorithm to play the Doom gameTeach your agent to play Connect4 using AlphaGo ZeroDefeat Atari arcade games using the value iteration methodDiscover how to deal with discrete and continuous action spaces in various environmentsWho this book is for If you’re an ML/DL enthusiast interested in AI and want to explore RL and deep RL from scratch, this Learning Path is for you. Prior knowledge of linear algebra is expected.
| Author | : Johnny Saldana |
| Publisher | : SAGE |
| Total Pages | : 282 |
| Release | : 2009-02-19 |
| Genre | : Reference |
| ISBN | : 1446200124 |
The Coding Manual for Qualitative Researchers is unique in providing, in one volume, an in-depth guide to each of the multiple approaches available for coding qualitative data. In total, 29 different approaches to coding are covered, ranging in complexity from beginner to advanced level and covering the full range of types of qualitative data from interview transcripts to field notes. For each approach profiled, Johnny Saldaña discusses the method’s origins in the professional literature, a description of the method, recommendations for practical applications, and a clearly illustrated example.
