The Llm Engineer S Playbook Mastering The Development Of Large Language Models For Real World Applications
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The LLM Engineer's Playbook: Mastering the Development of Large Language Models for Real-World Applications
The world of artificial intelligence is rapidly evolving, and at the heart of this revolution are Large Language Models (LLMs). These powerful tools are transforming how we interact with technology, offering unprecedented capabilities in natural language processing. The LLM Engineer's Playbook is an essential guide for anyone looking to navigate the complexities of developing and deploying LLMs in practical, real-world scenarios. This book provides a comprehensive roadmap for engineers, developers, and tech enthusiasts eager to harness the potential of LLMs, offering a blend of theoretical insights and hands-on techniques. Within these pages, you'll find a rich array of content designed to elevate your understanding and skills in LLM development. The book covers foundational concepts, ensuring even those new to the field can follow along, and progressively delves into more advanced topics. Key sections include the architecture and functioning of LLMs, data preparation and preprocessing, model training and fine-tuning, and best practices for deployment and maintenance. Each chapter is crafted to build on the previous one, creating a seamless learning experience. The practical examples and case studies illustrate how LLMs can be applied in various industries, from enhancing customer service chatbots to revolutionizing content creation and beyond.
Practical Natural Language Processing
Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective
Hands-On Machine Learning with C++
Author: Kirill Kolodiazhnyi
language: en
Publisher: Packt Publishing Ltd
Release Date: 2020-05-15
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 Features Become familiar with data processing, performance measuring, and model selection using various C++ libraries Implement practical machine learning and deep learning techniques to build smart models Deploy machine learning models to work on mobile and embedded devices Book DescriptionC++ 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 learn Explore how to load and preprocess various data types to suitable C++ data structures Employ key machine learning algorithms with various C++ libraries Understand the grid-search approach to find the best parameters for a machine learning model Implement an algorithm for filtering anomalies in user data using Gaussian distribution Improve collaborative filtering to deal with dynamic user preferences Use C++ libraries and APIs to manage model structures and parameters Implement a C++ program to solve image classification tasks with LeNet architecture Who 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.