Thimira Amaratunga (35 results)

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Paperback. Condition: New. 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.

Paperback. Condition: New. 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.

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Paperback or Softback. Condition: New. Understanding Large Language Models: Learning Their Underlying Concepts and Technologies. Book.

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Paperback. Condition: Brand New. 338 pages. 9.50x6.75x1.00 inches. In Stock.

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Paperback. Condition: New. 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.

Condition: New. 1st ed. edition NO-PA16APR2015-KAP.

Taschenbuch. Condition: Neu. Neuware -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 learningAPress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 176 pp. Englisch.

Paperback. Condition: New. 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.

Taschenbuch. Condition: Neu. Neuware -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. Yoüll 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 LearningWho This Book Is ForAI developers and enthusiasts wanting to work on the Windows platform.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 356 pp. Englisch.

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 176 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 theinternal-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 readingDeep 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 ForAI developers and enthusiasts wanting to work on the Windows platform. 356 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Beginning user level|Covers the risks and threats of misinterpretations of LLMsDiscusses architectures of GPT, BERT, PaLM, and LLaMAExplains tokenization, n-grams, recurrent networks, long short-term memory, and transformersTh.

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