This book equips you with the skills to harness the power of Generative AI, specifically Large Language Models (LLMs), to create various text formats. Get ready to experiment, explore, and unleash your creative potential! Part 1: Unveiling the Power of Generative AI and LLMs We'll begin by demystifying Generative AI and understanding how it transforms text creation. You'll then delve into the capabilities of LLMs, the powerhouse behind this technology. Through interactive exercises, you'll gain firsthand experience with pre-trained LLMs, exploring their strengths and limitations. Part 2: Mastering Text Generation Techniques Now that you're familiar with the tools, let's explore specific techniques for generating different text formats: Text Inpainting: Imagine restoring missing sections of historical documents or poems. We'll use LLMs to fill the gaps and reconstruct the text, putting your detective skills to the test with hands-on exercises. Text Summarization: Information overload is real! Learn how to leverage LLMs to create concise summaries of lengthy texts, like research papers or news articles. You'll practice generating summaries for presentations or reports through engaging exercises. Part 3: Unleashing Your Inner AI Artist Get ready to tap into your creative side! We'll explore how LLMs can assist you in crafting various artistic text formats: Storytelling: Spark your imagination with a starting line and see where the story unfolds! Prompt the LLM and collaborate on creating engaging narratives. Exercises will guide you in co-writing stories with an LLM, taking turns adding sentences. Poetry: Let the AI muse inspire you! We'll experiment with generating poems in different styles, from classic sonnets to modern haikus. Challenge yourself with themed haiku writing exercises using LLMs. Code Generation: Stuck on a coding problem? Discover how LLMs can become your coding assistant! We'll explore using LLMs for code completion and bug detection, putting their capabilities to the test with practical exercises. Part 4: Refining Your Craft - Advanced Techniques Ready to take your skills a step further? We'll delve into advanced techniques for generating more refined and controlled text outputs: Conditional Text Generation: Imagine guiding the LLM to create text that adheres to specific requirements. We'll experiment with specifying genre, style, or even keywords to influence the narrative in your exercises. Sampling Techniques: Discover how to generate diverse outputs from a single prompt! Explore different sampling techniques with LLMs and see how they impact the creativity and unexpectedness of the generated text. You'll compare and analyze outputs generated with different sampling methods. Style and Tone Control: Want your text to sound formal, funny, or even sarcastic? You'll learn how to control the stylistic elements of the generated text, tailoring it to your specific needs. Exercises will guide you in generating product descriptions with different writing styles. By the end of this workshop, you'll be a confident generative AI text creator, equipped with the skills to experiment with LLMs and produce creative, informative, and engaging text formats tailored to your needs

"LLM Transformers: A Practical Guide with Code, Tutorials, and Exercises" is your comprehensive companion to mastering Large Language Models (LLMs) and Transformers. This hands-on guide equips you with the knowledge and practical skills needed to understand, build, train, deploy, and maintain state-of-the-art language models. Starting with an introduction to the fundamentals of LLMs and Transformers, this book takes you on a journey through model training, fine-tuning, deployment strategies, and monitoring techniques. You'll explore popular frameworks such as TensorFlow, Keras, and PyTorch, learning how to implement and fine-tune LLMs for various natural language processing tasks. Each chapter is packed with code examples, step-by-step tutorials, and exercises designed to reinforce your learning and deepen your understanding. Whether you're a beginner looking to dive into the world of LLMs or an experienced practitioner seeking to enhance your skills, this book has something for everyone. By the end of "LLM Transformers: A Practical Guide with Code, Tutorials, and Exercises," you'll be equipped with the tools and knowledge needed to harness the power of LLMs and Transformers for your own projects, from chatbots and text summarization to question answering systems and beyond.

This book equips you to harness the remarkable capabilities of Large Language Models (LLMs) using Python. Part I unveils the world of LLMs. You'll delve into their inner workings, explore different LLM types, and discover their exciting applications in various fields. Part II dives into the practical side of things. We'll guide you through setting up your Python environment and interacting with LLMs. Learn to craft effective prompts to get the most out of LLMs and understand the different response formats they can generate. Part III gets you building! We'll explore how to leverage LLMs for creative text generation, from poems and scripts to code snippets. Craft effective question-answering systems and build engaging chatbots – the possibilities are endless! Part IV empowers you to maintain and improve your LLM creations. We'll delve into debugging techniques to identify and resolve issues. Learn to track performance and implement optimizations to ensure your LLM applications run smoothly. This book doesn't shy away from the bigger picture. The final chapter explores the ethical considerations of LLMs, addressing bias and promoting responsible use of this powerful technology. By the end of this journey, you'll be equipped to unlock the potential of LLMs with Python and contribute to a future brimming with exciting possibilities.

More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized

A surprisingly simple way for students to master any subject--based on one of the world's most popular online courses and the bestselling book A Mind for Numbers A Mind for Numbers and its wildly popular online companion course "Learning How to Learn" have empowered more than two million learners of all ages from around the world to master subjects that they once struggled with. Fans often wish they'd discovered these learning strategies earlier and ask how they can help their kids master these skills as well. Now in this new book for kids and teens, the authors reveal how to make the most of time spent studying. We all have the tools to learn what might not seem to come naturally to us at first--the secret is to understand how the brain works so we can unlock its power. This book explains: Why sometimes letting your mind wander is an important part of the learning process How to avoid "rut think" in order to think outside the box Why having a poor memory can be a good thing The value of metaphors in developing understanding A simple, yet powerful, way to stop procrastinating Filled with illustrations, application questions, and exercises, this book makes learning easy and fun.

Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key FeaturesBuild machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark and TensorFlowLearn model optimization, and understand how to scale your models using simple and secure APIsDevelop, train, tune and deploy neural network models to accelerate model performance in the cloudBook Description AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. What you will learnManage AI workflows by using AWS cloud to deploy services that feed smart data productsUse SageMaker services to create recommendation modelsScale model training and deployment using Apache Spark on EMRUnderstand how to cluster big data through EMR and seamlessly integrate it with SageMakerBuild deep learning models on AWS using TensorFlow and deploy them as servicesEnhance your apps by combining Apache Spark and Amazon SageMakerWho this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.

Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.