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Author: Anand Vemula Publisher: Anand Vemula ISBN: Category : Computers Languages : en Pages : 72
Book Description
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specific techniques for generating different text formats: Text Inpainting: Imagine restoring missing sections of historical documents or poems.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
Author: Anand Vemula Publisher: Anand Vemula ISBN: Category : Computers Languages : en Pages : 72
Book Description
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
Author: Anand Vemula Publisher: Independently Published ISBN: Category : Computers Languages : en Pages : 0
Book Description
"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.
Author: Anand Vemula Publisher: Anand Vemula ISBN: Category : Computers Languages : en Pages : 42
Book Description
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.
Author: Mark Treveil Publisher: "O'Reilly Media, Inc." ISBN: 1098116429 Category : Computers Languages : en Pages : 171
Book Description
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
Author: Jason Brownlee Publisher: Machine Learning Mastery ISBN: Category : Computers Languages : en Pages : 266
Book Description
Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this Ebook, learn exactly how to get started and apply deep learning to your own machine learning projects.
Author: Barbara Oakley, PhD Publisher: Penguin ISBN: 052550446X Category : Juvenile Nonfiction Languages : en Pages : 258
Book Description
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.
Author: Jason Brownlee Publisher: Machine Learning Mastery ISBN: Category : Computers Languages : en Pages : 162
Book Description
You must understand the algorithms to get good (and be recognized as being good) at machine learning. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work, then implement them from scratch, step-by-step.
Author: Joseph Babcock Publisher: Packt Publishing Ltd ISBN: 1800208502 Category : Computers Languages : en Pages : 489
Book Description
Fun and exciting projects to learn what artificial minds can create Key FeaturesCode examples are in TensorFlow 2, which make it easy for PyTorch users to follow alongLook inside the most famous deep generative models, from GPT to MuseGANLearn to build and adapt your own models in TensorFlow 2.xExplore exciting, cutting-edge use cases for deep generative AIBook Description Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI? In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks. There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation. What you will learnExport the code from GitHub into Google Colab to see how everything works for yourselfCompose music using LSTM models, simple GANs, and MuseGANCreate deepfakes using facial landmarks, autoencoders, and pix2pix GANLearn how attention and transformers have changed NLPBuild several text generation pipelines based on LSTMs, BERT, and GPT-2Implement paired and unpaired style transfer with networks like StyleGANDiscover emerging applications of generative AI like folding proteins and creating videos from imagesWho this book is for This is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.
Author: Dr. Saket S.R. Mengle Publisher: Packt Publishing Ltd ISBN: 1789347505 Category : Computers Languages : en Pages : 293
Book Description
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.
Author: Kirill Kolodiazhnyi Publisher: Packt Publishing Ltd ISBN: 1789952476 Category : Computers Languages : en Pages : 515
Book Description
Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets Key FeaturesBecome familiar with data processing, performance measuring, and model selection using various C++ librariesImplement practical machine learning and deep learning techniques to build smart modelsDeploy machine learning models to work on mobile and embedded devicesBook Description C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), showing you how to use C++ libraries to get the most out of your data. This book makes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement supervised and unsupervised ML algorithms through real-world examples. This book will get you hands-on with tuning and optimizing a model for different use cases, assisting you with model selection and the measurement of performance. You’ll cover techniques such as product recommendations, ensemble learning, and anomaly detection using modern C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Next, you’ll explore neural networks and deep learning using examples such as image classification and sentiment analysis, which will help you solve various problems. Later, you’ll learn how to handle production and deployment challenges on mobile and cloud platforms, before discovering how to export and import models using the ONNX format. By the end of this C++ book, you will have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems. What you will learnExplore how to load and preprocess various data types to suitable C++ data structuresEmploy key machine learning algorithms with various C++ librariesUnderstand the grid-search approach to find the best parameters for a machine learning modelImplement an algorithm for filtering anomalies in user data using Gaussian distributionImprove collaborative filtering to deal with dynamic user preferencesUse C++ libraries and APIs to manage model structures and parametersImplement a C++ program to solve image classification tasks with LeNet architectureWho this book is for You will find this C++ machine learning book useful if you want to get started with machine learning algorithms and techniques using the popular C++ language. As well as being a useful first course in machine learning with C++, this book will also appeal to data analysts, data scientists, and machine learning developers who are looking to implement different machine learning models in production using varied datasets and examples. Working knowledge of the C++ programming language is mandatory to get started with this book.
Author: Anand Vemula
Author: Anand Vemula
Author: Anand Vemula
Author: Anand Vemula
Author: Mark Treveil
Author: Jason Brownlee
Author: Barbara Oakley, PhD
Author: Jason Brownlee
Author: Joseph Babcock
Author: Dr. Saket S.R. Mengle
Author: Kirill Kolodiazhnyi