Automated_Machine_Learning_with_AutoKeras2021_Luis_Sobrecueva.pdf
6.1 MB
📙 Automated Machine Learning with AutoKeras: Deep learning made accessible for everyone with just few lines of coding [2021] Luis Sobrecueva
AutoKeras is an AutoML open-source software library that provides easy access to deep learning models. If you are looking to build deep learning model architectures and perform parameter tuning automatically using AutoKeras, then this book is for you. This book teaches you how to develop and use state-of-the-art AI algorithms in your projects. It begins with a high-level introduction to automated machine learning, explaining all the concepts required to get started with this machine learning approach. You will then learn how to use AutoKeras for image and text classification and regression. As you make progress, you'll discover how to use AutoKeras to perform sentiment analysis on documents. This book will also show you how to implement a custom model for topic classification with AutoKeras. Toward the end, you will explore advanced concepts of AutoKeras such as working with multi-modal data and multi-task, customizing the model with AutoModel, and visualizing experiment results using AutoKeras Extensions. By the end of this machine learning book, you will be able to confidently use AutoKeras to design your own custom machine learning models in your company. #программирование #алгоритмы #квантовые_вычисления #глубокое_обучение #deep_learning #искусственный_интеллект #AI #python
AutoKeras is an AutoML open-source software library that provides easy access to deep learning models. If you are looking to build deep learning model architectures and perform parameter tuning automatically using AutoKeras, then this book is for you. This book teaches you how to develop and use state-of-the-art AI algorithms in your projects. It begins with a high-level introduction to automated machine learning, explaining all the concepts required to get started with this machine learning approach. You will then learn how to use AutoKeras for image and text classification and regression. As you make progress, you'll discover how to use AutoKeras to perform sentiment analysis on documents. This book will also show you how to implement a custom model for topic classification with AutoKeras. Toward the end, you will explore advanced concepts of AutoKeras such as working with multi-modal data and multi-task, customizing the model with AutoModel, and visualizing experiment results using AutoKeras Extensions. By the end of this machine learning book, you will be able to confidently use AutoKeras to design your own custom machine learning models in your company. #программирование #алгоритмы #квантовые_вычисления #глубокое_обучение #deep_learning #искусственный_интеллект #AI #python
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📗 Computer Vision: Algorithms and Applications (Texts in Computer Science) [2022] Richard Szeliski
📗 Компьютерное зрение: алгоритмы и приложения [2022] Ричард Шелиски
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Компьютерное зрение: алгоритмы и приложения исследует разнообразие методов, используемых для анализа и интерпретации изображений. В нем также описываются сложные приложения реального мира, в которых успешно используется vision, как в специализированных приложениях, таких как поиск изображений и автономная навигация, так и для выполнения увлекательных задач потребительского уровня, которые учащиеся могут применять к своим личным фотографиям и видео. Этот исключительно авторитетный и всеобъемлющий учебник/ справочник представляет собой не просто источник “рецептов”, но и научный подход к постановке задач компьютерного зрения. Затем эти задачи анализируются с использованием новейших классических моделей и моделей глубокого обучения и решаются с использованием строгих инженерных принципов.
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💡 Physics.Math.Code // @physics_lib
📗 Компьютерное зрение: алгоритмы и приложения [2022] Ричард Шелиски
💾 Скачать книгу
Компьютерное зрение: алгоритмы и приложения исследует разнообразие методов, используемых для анализа и интерпретации изображений. В нем также описываются сложные приложения реального мира, в которых успешно используется vision, как в специализированных приложениях, таких как поиск изображений и автономная навигация, так и для выполнения увлекательных задач потребительского уровня, которые учащиеся могут применять к своим личным фотографиям и видео. Этот исключительно авторитетный и всеобъемлющий учебник/ справочник представляет собой не просто источник “рецептов”, но и научный подход к постановке задач компьютерного зрения. Затем эти задачи анализируются с использованием новейших классических моделей и моделей глубокого обучения и решаются с использованием строгих инженерных принципов.
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410012169999048
Карта ВТБ: 4272290768112195
Карта Сбербанк: 2202200638175206
#компьютерное_зрение #программирование #алгоритмы #глубокое_обучение #алгоритмы💡 Physics.Math.Code // @physics_lib
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Computer_Vision_Algorithms_and_Applications_2022_Richard_Szeliski.zip
402.7 MB
📗 Computer Vision: Algorithms and Applications [2022] Richard Szeliski
Computer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos.
More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference takes a scientific approach to the formulation of computer vision problems. These problems are then analyzed using the latest classical and deep learning models and solved using rigorous engineering principles.
Topics and features:
▪️ Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses
▪️ Incorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented reality
▪️ Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects
▪️ Includes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decade
▪️ Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, estimation theory, datasets, and software
Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.
💡 Physics.Math.Code // @physics_lib
Computer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos.
More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference takes a scientific approach to the formulation of computer vision problems. These problems are then analyzed using the latest classical and deep learning models and solved using rigorous engineering principles.
Topics and features:
▪️ Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses
▪️ Incorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented reality
▪️ Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects
▪️ Includes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decade
▪️ Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, estimation theory, datasets, and software
Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.
💡 Physics.Math.Code // @physics_lib
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