The advancement of Large Language Models (LLMs) has revolutionized the field of Natural Language Processing in recent years. Models like BERT, T5, and ChatGPT have demonstrated unprecedented performance on a wide range of NLP tasks, from text classification to machine translation. Despite their impressive performance, the use of LLMs remains challenging for many practitioners. The sheer size of these models, combined with the lack of understanding of their inner workings, has made it difficult for practitioners to effectively use and optimize these models for their specific needs. Quick Start Guide to Large Language Models: Strategies and Best Practices for using ChatGPT and Other LLMs is a practical guide to the use of LLMs in NLP. It provides an overview of the key concepts and techniques used in LLMs and explains how these models work and how they can be used for various NLP tasks. The book also covers advanced topics, such as fine-tuning, alignment, and information retrieval while providing practical tips and tricks for training and optimizing LLMs for specific NLP tasks. This work addresses a wide range of topics in the field of Large Language Models, including the basics of LLMs, launching an application with proprietary models, fine-tuning GPT3 with custom examples, prompt engineering, building a recommendation engine, combining Transformers, and deploying custom LLMs to the cloud. It offers an in-depth look at the various concepts, techniques, and tools used in the field of Large Language Models. Topics covered: Coding with Large Language Models (LLMs) Overview of using proprietary models OpenAI, Embeddings, GPT3, and ChatGPT Vector databases and building a neural/semantic information retrieval system Fine-tuning GPT3 with custom examples Prompt engineering with GPT3 and ChatGPT Advanced prompt engineering techniques Building a recommendation engine Combining Transformers Deploying custom LLMs to the cloud

Kubernetes is an essential tool for anyone deploying and managing cloud-native applications. It lays out a complete introduction to container technologies and containerized applications along with practical tips for efficient deployment and operation. This revised edition of the bestselling Kubernetes in Action contains new coverage of the Kubernetes architecture, including the Kubernetes API, and a deep dive into managing a Kubernetes cluster in production. In Kubernetes in Action, Second Edition, you'll start with an overview of how Docker containers work with Kubernetes and move quickly to building your first cluster. You'll gradually expand your initial application, adding features and deepening your knowledge of Kubernetes architecture and operation. As you navigate this comprehensive guide, you'll also appreciate thorough coverage of high-value topics like monitoring, tuning, and scaling Kubernetes in Action, Second Edition teaches you to use Kubernetes to deploy container-based distributed applications. You'll start with an overview of how Docker containers work with Kubernetes and move quickly to building your first cluster. You'll gradually expand your initial application, adding features and deepening your knowledge of Kubernetes architecture and operation. In this revised and expanded second edition, you'll take a deep dive into the structure of a Kubernetes-based application and discover how to manage a Kubernetes cluster in production. As you navigate this comprehensive guide, you'll also appreciate thorough coverage of high-value topics like monitoring, tuning, and scaling.

Get into the world of smart data security using machine learning algorithms and Python libraries Key Features Learn machine learning algorithms and cybersecurity fundamentals Automate your daily workflow by applying use cases to many facets of security Implement smart machine learning solutions to detect various cybersecurity problems Book DescriptionCyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain. The book begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs, and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not. Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems What you will learn Use machine learning algorithms with complex datasets to implement cybersecurity concepts Implement machine learning algorithms such as clustering, k-means, and Naive Bayes to solve real-world problems Learn to speed up a system using Python libraries with NumPy, Scikit-learn, and CUDA Understand how to combat malware, detect spam, and fight financial fraud to mitigate cyber crimes Use TensorFlow in the cybersecurity domain and implement real-world examples Learn how machine learning and Python can be used in complex cyber issues Who this book is forThis book is for the data scientists, machine learning developers, security researchers, and anyone keen to apply machine learning to up-skill computer security. Having some working knowledge of Python and being familiar with the basics of machine learning and cybersecurity fundamentals will help to get the most out of the book

A perfect guide to speed up the predicting power of machine learning algorithms Key Features Design, discover, and create dynamic, efficient features for your machine learning application Understand your data in-depth and derive astonishing data insights with the help of this Guide Grasp powerful feature-engineering techniques and build machine learning systems Book DescriptionFeature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective. You will start with understanding your data-often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data. By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization. What you will learn Identify and leverage different feature types Clean features in data to improve predictive power Understand why and how to perform feature selection, and model error analysis Leverage domain knowledge to construct new features Deliver features based on mathematical insights Use machine-learning algorithms to construct features Master feature engineering and optimization Harness feature engineering for real world applications through a structured case study Who this book is forIf you are a data science professional or a machine learning engineer looking to strengthen your predictive analytics model, then this book is a perfect guide for you. Some basic understanding of the machine learning concepts and Python scripting would be enough to get started with this book.

Learn the techniques and math you need to start making sense of your data About This Book * Enhance your knowledge of coding with data science theory for practical insight into data science and analysis * More than just a math class, learn how to perform real-world data science tasks with R and Python * Create actionable insights and transform raw data into tangible value Who This Book Is For You should be fairly well acquainted with basic algebra and should feel comfortable reading snippets of R/Python as well as pseudo code. You should have the urge to learn and apply the techniques put forth in this book on either your own data sets or those provided to you. If you have the basic math skills but want to apply them in data science or you have good programming skills but lack math, then this book is for you. What You Will Learn * Get to know the five most important steps of data science * Use your data intelligently and learn how to handle it with care * Bridge the gap between mathematics and programming * Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results * Build and evaluate baseline machine learning models * Explore the most effective metrics to determine the success of your machine learning models * Create data visualizations that communicate actionable insights * Read and apply machine learning concepts to your problems and make actual predictions In Detail Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking-and answering-complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means. Style and approach This is an easy-to-understand and accessible tutorial. It is a step-by-step guide with use cases, examples, and illustrations to get you well-versed with the concepts of data science. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts later on and will help you implement these techniques in the real world.