Bøger af Thimira Amaratunga

This book will teach you the underlying concepts of large language models (LLMs), as well as the technologies associated with them.The book starts with an introduction to the rise of conversational AIs such as ChatGPT, and how they are related to the broader spectrum of large language models. From there, you will learn about natural language processing (NLP), its core concepts, and how it has led to the rise of LLMs. Next, you will gain insight into transformers and how their characteristics, such as self-attention, enhance the capabilities of language modeling, along with the unique capabilities of LLMs. The book concludes with an exploration of the architectures of various LLMs and the opportunities presented by their ever-increasing capabilities¿as well as the dangers of their misuse.After completing this book, you will have a thorough understanding of LLMs and will be ready to take your first steps in implementing them into your own projects. What You Will LearnGrasp the underlying concepts of LLMsGain insight into how the concepts and approaches of NLP have evolved over the yearsUnderstand transformer models and attention mechanismsExplore different types of LLMs and their applicationsUnderstand the architectures of popular LLMsDelve into misconceptions and concerns about LLMs, as well as how to best utilize themWho This Book Is ForAnyone interested in learning the foundational concepts of NLP, LLMs, and recent advancements of deep learning

New 2019 Edition!Build Deeper is a complete and practical guide that can help you take the first few steps in deep learning. It will guide you step-by-step, from understanding the basic concepts, to building your first practical application.It covers: What Deep Learning is, and where it fits with Artificial Intelligence and Machine Learning.How Deep Learning came to be, its predecessors, and the path it took to evolve into what it is today.The important milestones it has passed through the years, and the impact they had on the field.What tools are available for us to learn and build deep learning applications, and how to set them up: Python, TensorFlow, Theano, Keras, and more, on any OS of your choosing: Windows, Linux, or Mac OS.Building our first simple deep learning model.The internal workings of a deep learning model.Using more advanced topics such as Data Augmentation, Transfer Learning, Bottleneck Features, and Fine Tuning to build a practical deep learning application.Getting started with Computer Vision.All you need now is a little enthusiasm ... who knows where it will take you!Go a little deeper to discover ...

Build deep learning and computer vision systems using Python, TensorFlow, Keras, OpenCV, and more, right within the familiar environment of Microsoft Windows. The book starts with an introduction to tools for deep learning and computer vision tasks followed by instructions to install, configure, and troubleshoot them. Here, you will learn how Python can help you build deep learning models on Windows. Moving forward, you will build a deep learning model and understand the internal-workings of a convolutional neural network on Windows. Further, you will go through different ways to visualize the internal-workings of deep learning models along with an understanding of transfer learning where you will learn how to build model architecture and use data augmentations. Next, you will manage and train deep learning models on Windows before deploying your application as a web application. You'll also do some simple image processing and work with computer vision options that will help you build various applications with deep learning. Finally, you will use generative adversarial networks along with reinforcement learning. After reading Deep Learning on Windows, you will be able to design deep learning models and web applications on the Windows operating system.What You Will LearnUnderstand the basics of Deep Learning and its historyGet Deep Learning tools working on Microsoft WindowsUnderstand the internal-workings of Deep Learning models by using model visualization techniques, such as the built-in plot_model function of Keras and third-party visualization toolsUnderstand Transfer Learning and how to utilize it to tackle small datasetsBuild robust training scripts to handle long-running training jobsConvert your Deep Learning model into a web applicationGenerate handwritten digits and human faces with DCGAN (Deep Convolutional Generative Adversarial Network)Understand the basics of Reinforcement Learning Who This Book Is For AI developers and enthusiasts wanting to work on the Windows platform.