Agrawal tanay (29 resultados)

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Paperback. Condición: New. Second Edition. This new edition of Building a Virtual Assistant for Raspberry Pi will provide new features to your virtual assistant, Melissa, adding advanced capabilities to her. Updated to the latest Python release (Python 3), the book will follow the same structure as its previous edition with new features discussed as you move forward.After walking you through the various STTs, TTSs, and implementation of these components with the Python 3, you will learn about Large Language Models (LLMs) and how to run them on Raspberry Pi. You will build applications/modules for Melissa that will be used by the LLM. The book will feature Plugin Architecture Implementation for Melissa, allowing users to script their own interactions with Melissa. You will also enable Melissa to interact with resources on the internet. In addition to Melissa's voice controlled interface for notes management, this edition will also enable Melissa to be intelligent to assist you with your tasks.Adding to Melissa's voice interface from the first edition, you will use an LLM to make Melissa intelligent. You will learn about function calling, providing Melissa access to tools. In conclusion, you will make a smart virtual assistant that runs on Raspberry Pi. What You'll LearnImplement plugin architecture to enable quicker scriptingDevelop modules for conversational interaction with the assistantRun the local LLM instance to empower Melissa with intelligenceInteract with both public and restricted APIs (Application Programming Interfaces)Who This Book Is ForAnyone who has built a home automation project with Raspberry Pi and now want to enhance it by making it voice-controlled. The book would also interest students from computer science or related disciplines.…

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Paperback or Softback. Condición: New. Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient. Book.

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Condición: As New. Unread book in perfect condition.

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Paperback. Condición: New. Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods.This is a step-by-step guide to hyperparameter optimization, starting with what hyperparameters are and how they affect different aspects of machine learning models. It then goes through some basic (brute force) algorithms of hyperparameter optimization. Further, the author addresses the problem of time and memory constraints, using distributed optimization methods. Next you'll discuss Bayesian optimization for hyperparameter search, which learns from its previous history. The book discusses different frameworks, such as Hyperopt and Optuna, which implements sequential model-based global optimization (SMBO) algorithms. During these discussions, you'll focus on different aspects such as creation of search spaces and distributed optimization of these libraries. Hyperparameter Optimization in Machine Learning creates an understanding of how these algorithms work and how you can use them in real-life data science problems. The final chapter summaries the role of hyperparameter optimization in automated machine learning and ends with a tutorial to create your own AutoML script.Hyperparameter optimization is tedious task, so sit back and let these algorithms do your work. What You Will LearnDiscover how changes in hyperparameters affect the model's performance.Apply different hyperparameter tuning algorithms to data science problemsWork with Bayesian optimization methods to create efficient machine learning and deep learning modelsDistribute hyperparameter optimization using a cluster of machinesApproach automated machine learning using hyperparameter optimizationWho This Book Is For Professionals and students working with machine learning.…

Condición: New. 2020. 1st ed. paperback. . . . . .

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

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Paperback. Condición: Brand New. 166 pages. 9.00x6.00x0.50 inches. In Stock.

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Condición: New. 2020. 1st ed. paperback. . . . . . Books ship from the US and Ireland.

Paperback. Condición: New. Second Edition. This new edition of Building a Virtual Assistant for Raspberry Pi will provide new features to your virtual assistant, Melissa, adding advanced capabilities to her. Updated to the latest Python release (Python 3), the book will follow the same structure as its previous edition with new features discussed as you move forward.After walking you through the various STTs, TTSs, and implementation of these components with the Python 3, you will learn about Large Language Models (LLMs) and how to run them on Raspberry Pi. You will build applications/modules for Melissa that will be used by the LLM. The book will feature Plugin Architecture Implementation for Melissa, allowing users to script their own interactions with Melissa. You will also enable Melissa to interact with resources on the internet. In addition to Melissa's voice controlled interface for notes management, this edition will also enable Melissa to be intelligent to assist you with your tasks.Adding to Melissa's voice interface from the first edition, you will use an LLM to make Melissa intelligent. You will learn about function calling, providing Melissa access to tools. In conclusion, you will make a smart virtual assistant that runs on Raspberry Pi. What You'll LearnImplement plugin architecture to enable quicker scriptingDevelop modules for conversational interaction with the assistantRun the local LLM instance to empower Melissa with intelligenceInteract with both public and restricted APIs (Application Programming Interfaces)Who This Book Is ForAnyone who has built a home automation project with Raspberry Pi and now want to enhance it by making it voice-controlled. The book would also interest students from computer science or related disciplines.…

Taschenbuch. Condición: Neu. Hyperparameter Optimization in Machine Learning | Make Your Machine Learning and Deep Learning Models More Efficient | Tanay Agrawal | Taschenbuch | xix | Englisch | 2020 | Apress | EAN 9781484265789 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Paperback. Condición: New. Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods.This is a step-by-step guide to hyperparameter optimization, starting with what hyperparameters are and how they affect different aspects of machine learning models. It then goes through some basic (brute force) algorithms of hyperparameter optimization. Further, the author addresses the problem of time and memory constraints, using distributed optimization methods. Next you'll discuss Bayesian optimization for hyperparameter search, which learns from its previous history. The book discusses different frameworks, such as Hyperopt and Optuna, which implements sequential model-based global optimization (SMBO) algorithms. During these discussions, you'll focus on different aspects such as creation of search spaces and distributed optimization of these libraries. Hyperparameter Optimization in Machine Learning creates an understanding of how these algorithms work and how you can use them in real-life data science problems. The final chapter summaries the role of hyperparameter optimization in automated machine learning and ends with a tutorial to create your own AutoML script.Hyperparameter optimization is tedious task, so sit back and let these algorithms do your work. What You Will LearnDiscover how changes in hyperparameters affect the model's performance.Apply different hyperparameter tuning algorithms to data science problemsWork with Bayesian optimization methods to create efficient machine learning and deep learning modelsDistribute hyperparameter optimization using a cluster of machinesApproach automated machine learning using hyperparameter optimizationWho This Book Is For Professionals and students working with machine learning.…

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PF. Condición: New. This item is printed on demand. Brand new book, sourced directly from publisher. Dispatch time is 6-7 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods.This is a step-by-step guide to hyperparameter optimization, starting with what hyperparameters are and how they affect different aspects of machine learning models. It then goes through some basic (brute force) algorithms of hyperparameter optimization. Further, the author addresses the problem of time and memory constraints, using distributed optimization methods. Next you'll discuss Bayesian optimization for hyperparameter search, which learns from its previous history. The book discusses different frameworks, such as Hyperopt and Optuna, which implements sequential model-based global optimization (SMBO) algorithms. During these discussions, you'll focus on different aspects such as creation of search spaces and distributed optimization of these libraries. Hyperparameter Optimization in Machine Learning creates an understanding of how these algorithms work and how you can use them in real-life data science problems. The final chapter summaries the role of hyperparameter optimization in automated machine learning and ends with a tutorial to create your own AutoML script.Hyperparameter optimization is tedious task, so sit back and let these algorithms do your work.What You Will LearnDiscover how changes in hyperparameters affect the model's performance.Apply different hyperparameter tuning algorithms to data science problemsWork with Bayesian optimization methods to create efficient machine learning and deep learning modelsDistribute hyperparameter optimization using a cluster of machinesApproach automated machine learning using hyperparameter optimizationWho This Book Is ForProfessionals and students working with machine learning. 188 pp. Englisch.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods.This is a step-by-step guide to hyperparameter optimization, starting with what hyperparameters are and how they affect different aspects of machine learning models. It then goes through some basic (brute force) algorithms of hyperparameter optimization. Further, the author addresses the problem of time and memory constraints, using distributed optimization methods. Next you'll discuss Bayesian optimization for hyperparameter search, which learns from its previous history. The book discusses different frameworks, such as Hyperopt and Optuna, which implements sequential model-based global optimization (SMBO) algorithms. During these discussions, you'll focus on different aspects such as creation of search spaces and distributed optimization of these libraries. Hyperparameter Optimization in Machine Learning creates an understanding of how these algorithms work and how you can use them in real-life data science problems. The final chapter summaries the role of hyperparameter optimization in automated machine learning and ends with a tutorial to create your own AutoML script.Hyperparameter optimization is tedious task, so sit back and let these algorithms do your work.What You Will LearnDiscover how changes in hyperparameters affect the model's performance.Apply different hyperparameter tuning algorithms to data science problemsWork with Bayesian optimization methods to create efficient machine learning and deep learning modelsDistribute hyperparameter optimization using a cluster of machinesApproach automated machine learning using hyperparameter optimizationWho This Book Is ForProfessionals and students working with machine learning.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Covers state-of-the-art techniques for hyperparameter tuningCovers implementation of advanced Bayesian optimization techniques on machine learning algorithms to complex deep learning frameworksExplains distr.…

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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods.This is a step-by-step guide to hyperparameter optimization, starting with what hyperparameters are and how they affect different aspects of machine learning models. It then goes through some basic (brute force) algorithms of hyperparameter optimization. Further, the author addresses the problem of time and memory constraints, using distributed optimization methods. Next you'll discuss Bayesian optimization for hyperparameter search, which learns from its previous history.The book discusses different frameworks, such as Hyperopt and Optuna, which implements sequential model-based global optimization (SMBO) algorithms. During these discussions, you'll focus on different aspects such as creation of search spaces and distributed optimization of these libraries.Hyperparameter Optimization in Machine Learning creates an understanding of how these algorithms work and how you can use them in real-life data science problems. The final chapter summaries the role of hyperparameter optimization in automated machine learning and ends with a tutorial to create your own AutoML script.Hyperparameter optimization is tedious task, so sit back and let these algorithms do your work.What You Will LearnDiscover how changes in hyperparameters affect the model's performance.Apply different hyperparameter tuning algorithms to data science problemsWork with Bayesian optimization methods to create efficient machine learning and deep learning modelsDistribute hyperparameter optimization using a cluster of machinesApproach automated machine learning using hyperparameter optimizationSpringer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 188 pp. Englisch.…