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Become a prompt engineer with the help of this practical guide. With broad applicability across various topics such as copywriting, SEO, book writing, fiction, and non-fiction, this comprehensive guide provides valuable insights for anyone interested in exploring the art of prompt engineering. Learn practical strategies to monetize your use of ChatGPT while enhancing your writing and communication skills. Boost the efficiency and productivity of content creation by implementing the actionable knowledge gained from this book.
Book Synopsis The Secrets of ChatGPT Prompt Engineering for Non-Developers by : Cea West
Download or read book The Secrets of ChatGPT Prompt Engineering for Non-Developers written by Cea West and published by Cea West. This book was released on with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Become a prompt engineer with the help of this practical guide. With broad applicability across various topics such as copywriting, SEO, book writing, fiction, and non-fiction, this comprehensive guide provides valuable insights for anyone interested in exploring the art of prompt engineering. Learn practical strategies to monetize your use of ChatGPT while enhancing your writing and communication skills. Boost the efficiency and productivity of content creation by implementing the actionable knowledge gained from this book.
A practical approach to Prompt Engineering for developers. Dive into the world of Prompt Engineering agility, optimizing your prompts for dynamic LLM interactions. Learn with hands-on examples from the real world and elevate your developer experience with LLMs. Discover how the right prompts can revolutionize your interactions with LLMs. In "LLM Prompt Engineering For Developers," we take a comprehensive journey into the world of LLMs and the art of crafting effective prompts for them. The guide starts by laying the foundation, exploring the evolution of Natural Language Processing (NLP) from its early days to the sophisticated LLMs we interact with today. You will dive deep into the complexities of models such as GPT models, understanding their architecture, capabilities, and nuances. As we progress, this guide emphasizes the importance of effective prompt engineering and its best practices. While LLMs like ChatGPT (GPT-3.5 and GPT-4) are powerful, their full potential is only realized when they are communicated with effectively. This is where prompt engineering comes into play. It's not simply about asking the model a question; it's about phrasing, context, and understanding the model's logic. Through chapters dedicated to Azure Prompt Flow, LangChain, and other tools, you'll gain hands-on experience in crafting, testing, scoring and optimizing prompts. We'll also explore advanced concepts like Few-shot Learning, Chain of Thought, Perplexity and techniques like ReAct and General Knowledge Prompting, equipping you with a comprehensive understanding of the domain. This guide is designed to be hands-on, offering practical insights and exercises. In fact, as you progress, you'll familiarize yourself with several tools: openai Python library: You will dive into the core of OpenAI's LLMs and learn how to interact and fine-tune models to achieve precise outputs tailored to specific needs. promptfoo: You will master the art of crafting effective prompts. Throughout the guide, we'll use promptfoo to test and score prompts, ensuring they're optimized for desired outcomes. LangChain: You'll explore the LangChain framework, which elevates LLM-powered applications. You'll dive into understanding how a prompt engineer can leverage the power of this tool to test and build effective prompts. betterprompt: Before deploying, it's essential to test. With betterprompt, you'll ensure the LLM prompts are ready for real-world scenarios, refining them as needed. Azure Prompt Flow: You will experience the visual interface of Azure's tool, streamlining LLM-based AI development. You'll design executable flows, integrating LLMs, prompts, and Python tools, ensuring a holistic understanding of the art of prompting. And more! With these tools in your toolkit, you will be well-prepared to craft powerful and effective prompts. The hands-on exercises will help solidify your understanding. Throughout the process, you'll be actively engaged and by the end, not only will you appreciate the power of prompt engineering, but you'll also possess the skills to implement it effectively.
Book Synopsis LLM Prompt Engineering For Developers by : Aymen El Amri
Download or read book LLM Prompt Engineering For Developers written by Aymen El Amri and published by Independently Published. This book was released on 2023-09 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical approach to Prompt Engineering for developers. Dive into the world of Prompt Engineering agility, optimizing your prompts for dynamic LLM interactions. Learn with hands-on examples from the real world and elevate your developer experience with LLMs. Discover how the right prompts can revolutionize your interactions with LLMs. In "LLM Prompt Engineering For Developers," we take a comprehensive journey into the world of LLMs and the art of crafting effective prompts for them. The guide starts by laying the foundation, exploring the evolution of Natural Language Processing (NLP) from its early days to the sophisticated LLMs we interact with today. You will dive deep into the complexities of models such as GPT models, understanding their architecture, capabilities, and nuances. As we progress, this guide emphasizes the importance of effective prompt engineering and its best practices. While LLMs like ChatGPT (GPT-3.5 and GPT-4) are powerful, their full potential is only realized when they are communicated with effectively. This is where prompt engineering comes into play. It's not simply about asking the model a question; it's about phrasing, context, and understanding the model's logic. Through chapters dedicated to Azure Prompt Flow, LangChain, and other tools, you'll gain hands-on experience in crafting, testing, scoring and optimizing prompts. We'll also explore advanced concepts like Few-shot Learning, Chain of Thought, Perplexity and techniques like ReAct and General Knowledge Prompting, equipping you with a comprehensive understanding of the domain. This guide is designed to be hands-on, offering practical insights and exercises. In fact, as you progress, you'll familiarize yourself with several tools: openai Python library: You will dive into the core of OpenAI's LLMs and learn how to interact and fine-tune models to achieve precise outputs tailored to specific needs. promptfoo: You will master the art of crafting effective prompts. Throughout the guide, we'll use promptfoo to test and score prompts, ensuring they're optimized for desired outcomes. LangChain: You'll explore the LangChain framework, which elevates LLM-powered applications. You'll dive into understanding how a prompt engineer can leverage the power of this tool to test and build effective prompts. betterprompt: Before deploying, it's essential to test. With betterprompt, you'll ensure the LLM prompts are ready for real-world scenarios, refining them as needed. Azure Prompt Flow: You will experience the visual interface of Azure's tool, streamlining LLM-based AI development. You'll design executable flows, integrating LLMs, prompts, and Python tools, ensuring a holistic understanding of the art of prompting. And more! With these tools in your toolkit, you will be well-prepared to craft powerful and effective prompts. The hands-on exercises will help solidify your understanding. Throughout the process, you'll be actively engaged and by the end, not only will you appreciate the power of prompt engineering, but you'll also possess the skills to implement it effectively.
Super Founders uses a data-driven approach to understand what really differentiates billion-dollar startups from the rest—revealing that nearly everything we thought was true about them is false! Ali Tamaseb has spent thousands of hours manually amassing what may be the largest dataset ever collected on startups, comparing billion-dollar startups with those that failed to become one—30,000 data points on nearly every factor: number of competitors, market size, the founder’s age, his or her university’s ranking, quality of investors, fundraising time, and many, many more. And what he found looked far different than expected. Just to mention a few: Most unicorn founders had no industry experience; There's no disadvantage to being a solo founder or to being a non-technical CEO; Less than 15% went through any kind of accelerator program; Over half had strong competitors when starting--being first to market with an idea does not actually matter. You will also hear the stories of the early days of billion-dollar startups first-hand. The book includes exclusive interviews with the founders/investors of Zoom, Instacart, PayPal, Nest, Github, Flatiron Health, Kite Pharma, Facebook, Stripe, Airbnb, YouTube, LinkedIn, Lyft, DoorDash, Coinbase, and Square, venture capital investors like Elad Gil, Peter Thiel, Alfred Lin from Sequoia Capital and Keith Rabois of Founders Fund, as well as previously untold stories about the early days of ByteDance (TikTok), WhatsApp, Dropbox, Discord, DiDi, Flipkart, Instagram, Careem, Peloton, and SpaceX. Packed with counterintuitive insights and inside stories from people who have built massively successful companies, Super Founders is a paradigm-shifting and actionable guide for entrepreneurs, investors, and anyone interested in what makes a startup successful.
Book Synopsis Super Founders by : Ali Tamaseb
Download or read book Super Founders written by Ali Tamaseb and published by PublicAffairs. This book was released on 2021-05-18 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Super Founders uses a data-driven approach to understand what really differentiates billion-dollar startups from the rest—revealing that nearly everything we thought was true about them is false! Ali Tamaseb has spent thousands of hours manually amassing what may be the largest dataset ever collected on startups, comparing billion-dollar startups with those that failed to become one—30,000 data points on nearly every factor: number of competitors, market size, the founder’s age, his or her university’s ranking, quality of investors, fundraising time, and many, many more. And what he found looked far different than expected. Just to mention a few: Most unicorn founders had no industry experience; There's no disadvantage to being a solo founder or to being a non-technical CEO; Less than 15% went through any kind of accelerator program; Over half had strong competitors when starting--being first to market with an idea does not actually matter. You will also hear the stories of the early days of billion-dollar startups first-hand. The book includes exclusive interviews with the founders/investors of Zoom, Instacart, PayPal, Nest, Github, Flatiron Health, Kite Pharma, Facebook, Stripe, Airbnb, YouTube, LinkedIn, Lyft, DoorDash, Coinbase, and Square, venture capital investors like Elad Gil, Peter Thiel, Alfred Lin from Sequoia Capital and Keith Rabois of Founders Fund, as well as previously untold stories about the early days of ByteDance (TikTok), WhatsApp, Dropbox, Discord, DiDi, Flipkart, Instagram, Careem, Peloton, and SpaceX. Packed with counterintuitive insights and inside stories from people who have built massively successful companies, Super Founders is a paradigm-shifting and actionable guide for entrepreneurs, investors, and anyone interested in what makes a startup successful.
Maybe you’re a great programmer or IT professional, but marketing isn’t your thing. Or perhaps you’re a tech-savvy search engine marketer who wants a peek under the hood of a search engine optimized web site. Search engine marketing is a field where technology and marketing are both critical and interdependent, because small changes in the implementation of a web site can make you or break you in search engine rankings. Furthermore, the fusion of technology and marketing know-how can create web site features that attract more visitors. The mission of this book is to help web developers create web sites that rank well with the major search engines, and to teach search engine marketers how to use technology to their advantage. We assert that neither marketing nor IT can exist in a vacuum, and it is essential that they not see themselves as opposing forces in an organization. They must work together. This book aims to educate both sides in that regard.
Book Synopsis Professional Search Engine Optimization with PHP by : Cristian Darie
Download or read book Professional Search Engine Optimization with PHP written by Cristian Darie and published by John Wiley & Sons. This book was released on 2007-04-18 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: Maybe you’re a great programmer or IT professional, but marketing isn’t your thing. Or perhaps you’re a tech-savvy search engine marketer who wants a peek under the hood of a search engine optimized web site. Search engine marketing is a field where technology and marketing are both critical and interdependent, because small changes in the implementation of a web site can make you or break you in search engine rankings. Furthermore, the fusion of technology and marketing know-how can create web site features that attract more visitors. The mission of this book is to help web developers create web sites that rank well with the major search engines, and to teach search engine marketers how to use technology to their advantage. We assert that neither marketing nor IT can exist in a vacuum, and it is essential that they not see themselves as opposing forces in an organization. They must work together. This book aims to educate both sides in that regard.
Now completely updated for the enhanced multimedia, image-mapping, and style sheet capabilities of HTML 4.01, this lively guide reveals the secrets of award-winning Web designersóand shows you, step by step, how to create sticky, state-of-the-art Web sites. The CD-ROM comes with TidyHTML, the award-winning CoffeeCup suite, and other valuable Web design tools.
Book Synopsis Creating Cool HTML 4 Web Pages by : Dave Taylor
Download or read book Creating Cool HTML 4 Web Pages written by Dave Taylor and published by Wiley. This book was released on 2000-07-03 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now completely updated for the enhanced multimedia, image-mapping, and style sheet capabilities of HTML 4.01, this lively guide reveals the secrets of award-winning Web designersóand shows you, step by step, how to create sticky, state-of-the-art Web sites. The CD-ROM comes with TidyHTML, the award-winning CoffeeCup suite, and other valuable Web design tools.
The book introduces key Artificial Intelligence (AI) concepts in an easy-to-read format with examples and illustrations. A complex, long, overly mathematical textbook does not always serve the purpose of conveying the basic AI concepts to most people. Someone with basic knowledge in Computer Science can have a quick overview of AI (heuristic searches, genetic algorithms, expert systems, game trees, fuzzy expert systems, natural language processing, super intelligence, etc.) with everyday examples. If you are taking a basic AI course and find the traditional AI textbooks intimidating, you may choose this as a “bridge” book, or as an introductory textbook. For students, there is a lower priced edition (ISBN 978-1944708016) of the same book. Published by CSTrends LLP.
Book Synopsis Artificial Intelligence Simplified by : Binto George
Download or read book Artificial Intelligence Simplified written by Binto George and published by CSTrends LLP. This book was released on 2016-01-08 with total page 1 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book introduces key Artificial Intelligence (AI) concepts in an easy-to-read format with examples and illustrations. A complex, long, overly mathematical textbook does not always serve the purpose of conveying the basic AI concepts to most people. Someone with basic knowledge in Computer Science can have a quick overview of AI (heuristic searches, genetic algorithms, expert systems, game trees, fuzzy expert systems, natural language processing, super intelligence, etc.) with everyday examples. If you are taking a basic AI course and find the traditional AI textbooks intimidating, you may choose this as a “bridge” book, or as an introductory textbook. For students, there is a lower priced edition (ISBN 978-1944708016) of the same book. Published by CSTrends LLP.
A text which poses the question; what does modern science and the world's religions tell us about the mystery of life after death? This book explores these issues, enabling readers to experience one soul's journey through the afterlife.
Book Synopsis What Happens After Death by : Migene González-Wippler
Download or read book What Happens After Death written by Migene González-Wippler and published by Llewellyn Worldwide. This book was released on 1997 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: A text which poses the question; what does modern science and the world's religions tell us about the mystery of life after death? This book explores these issues, enabling readers to experience one soul's journey through the afterlife.
Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.
Book Synopsis Supervised Learning with Quantum Computers by : Maria Schuld
Download or read book Supervised Learning with Quantum Computers written by Maria Schuld and published by Springer. This book was released on 2018-08-30 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.
Learn the concepts of time series from traditional to bleeding-edge techniques. This book uses comprehensive examples to clearly illustrate statistical approaches and methods of analyzing time series data and its utilization in the real world. All the code is available in Jupyter notebooks. You'll begin by reviewing time series fundamentals, the structure of time series data, pre-processing, and how to craft the features through data wrangling. Next, you'll look at traditional time series techniques like ARMA, SARIMAX, VAR, and VARMA using trending framework like StatsModels and pmdarima. The book also explains building classification models using sktime, and covers advanced deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow. It concludes by explaining the popular framework fbprophet for modeling time series analysis. After reading Hands -On Time Series Analysis with Python, you'll be able to apply these new techniques in industries, such as oil and gas, robotics, manufacturing, government, banking, retail, healthcare, and more. What You'll Learn: · Explains basics to advanced concepts of time series · How to design, develop, train, and validate time-series methodologies · What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results · Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder to solve both Univariate and multivariate problems by using two types of data preparation methods for time series. · Univariate and multivariate problem solving using fbprophet. Who This Book Is For Data scientists, data analysts, financial analysts, and stock market researchers
Book Synopsis Hands-on Time Series Analysis with Python by : B V Vishwas
Download or read book Hands-on Time Series Analysis with Python written by B V Vishwas and published by Apress. This book was released on 2020-08-25 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the concepts of time series from traditional to bleeding-edge techniques. This book uses comprehensive examples to clearly illustrate statistical approaches and methods of analyzing time series data and its utilization in the real world. All the code is available in Jupyter notebooks. You'll begin by reviewing time series fundamentals, the structure of time series data, pre-processing, and how to craft the features through data wrangling. Next, you'll look at traditional time series techniques like ARMA, SARIMAX, VAR, and VARMA using trending framework like StatsModels and pmdarima. The book also explains building classification models using sktime, and covers advanced deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow. It concludes by explaining the popular framework fbprophet for modeling time series analysis. After reading Hands -On Time Series Analysis with Python, you'll be able to apply these new techniques in industries, such as oil and gas, robotics, manufacturing, government, banking, retail, healthcare, and more. What You'll Learn: · Explains basics to advanced concepts of time series · How to design, develop, train, and validate time-series methodologies · What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results · Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder to solve both Univariate and multivariate problems by using two types of data preparation methods for time series. · Univariate and multivariate problem solving using fbprophet. Who This Book Is For Data scientists, data analysts, financial analysts, and stock market researchers
Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key FeaturesExplore the most advanced deep learning techniques that drive modern AI resultsNew coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentationCompletely updated for TensorFlow 2.xBook Description Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. What you will learnUse mutual information maximization techniques to perform unsupervised learningUse segmentation to identify the pixel-wise class of each object in an imageIdentify both the bounding box and class of objects in an image using object detectionLearn the building blocks for advanced techniques - MLPss, CNN, and RNNsUnderstand deep neural networks - including ResNet and DenseNetUnderstand and build autoregressive models – autoencoders, VAEs, and GANsDiscover and implement deep reinforcement learning methodsWho this book is for This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be helpful. Knowledge of Keras or TensorFlow 2.0 is not required but is recommended.
Book Synopsis Advanced Deep Learning with TensorFlow 2 and Keras by : Rowel Atienza
Download or read book Advanced Deep Learning with TensorFlow 2 and Keras written by Rowel Atienza and published by Packt Publishing Ltd. This book was released on 2020-02-28 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key FeaturesExplore the most advanced deep learning techniques that drive modern AI resultsNew coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentationCompletely updated for TensorFlow 2.xBook Description Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. What you will learnUse mutual information maximization techniques to perform unsupervised learningUse segmentation to identify the pixel-wise class of each object in an imageIdentify both the bounding box and class of objects in an image using object detectionLearn the building blocks for advanced techniques - MLPss, CNN, and RNNsUnderstand deep neural networks - including ResNet and DenseNetUnderstand and build autoregressive models – autoencoders, VAEs, and GANsDiscover and implement deep reinforcement learning methodsWho this book is for This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be helpful. Knowledge of Keras or TensorFlow 2.0 is not required but is recommended.
