BOOKS - PROGRAMMING - Scikit-learn in Details Deep understanding
Scikit-learn in Details Deep understanding - Robert Collins 2018 EPUB | RTF | PDF CONV Amazon Digital Services LLC BOOKS PROGRAMMING
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Scikit-learn in Details Deep understanding
Author: Robert Collins
Year: 2018
Format: EPUB | RTF | PDF CONV
File size: 10.1 MB
Language: ENG



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Scikit-learn in details deep understanding is a book that explores the intricacies of machine learning algorithms and their applications in various fields. The book delves into the technical aspects of these algorithms, providing readers with a comprehensive understanding of the subject matter. It covers topics such as data preprocessing, feature selection, model evaluation, and hyperparameter tuning, among others. The author emphasizes the importance of mastering the underlying principles of machine learning to effectively utilize these techniques in real-world scenarios. The book begins by introducing the fundamental concepts of machine learning and its relevance in today's world. It discusses how technology has evolved over time and the impact it has had on society. The author highlights the need for individuals to develop a personal paradigm for perceiving technological advancements, particularly in the field of artificial intelligence (AI). This paradigm should be based on the survival of humanity and the unification of people in a warring state. The author then delves into the process of developing modern knowledge, which is essential for harnessing the power of AI. They argue that this process requires a deep understanding of the underlying principles of machine learning, including neural networks, decision trees, and clustering algorithms.
Scikit-learn in details deep understanding - книга, исследующая тонкости алгоритмов машинного обучения и их применения в различных областях. Книга углубляется в технические аспекты этих алгоритмов, предоставляя читателям исчерпывающее понимание предмета. Он охватывает такие темы, как предварительная обработка данных, выбор функций, оценка модели и настройка гиперпараметров и другие. Автор подчеркивает важность освоения основополагающих принципов машинного обучения для эффективного использования этих методов в реальных сценариях. Книга начинается с введения фундаментальных концепций машинного обучения и его актуальности в современном мире. В нем обсуждается, как технологии развивались с течением времени и какое влияние они оказали на общество. Автор подчеркивает необходимость для людей разработать личную парадигму для восприятия технологических достижений, особенно в области искусственного интеллекта (ИИ). Эта парадигма должна основываться на выживании человечества и объединении людей в воюющее государство. Затем автор углубляется в процесс развития современного знания, необходимого для использования силы ИИ. Они утверждают, что этот процесс требует глубокого понимания основополагающих принципов машинного обучения, включая нейронные сети, деревья решений и алгоритмы кластеризации.
Scikit-learn in details deep understanding es un libro que explora las sutilezas de los algoritmos de aprendizaje automático y sus aplicaciones en diversos campos. libro profundiza en los aspectos técnicos de estos algoritmos, proporcionando a los lectores una comprensión exhaustiva del tema. Abarca temas como el pre-procesamiento de datos, selección de funciones, evaluación de modelos y personalización de hiperparámetros, entre otros. autor subraya la importancia de dominar los principios fundamentales del aprendizaje automático para el uso efectivo de estas técnicas en escenarios reales. libro comienza con la introducción de los conceptos fundamentales del aprendizaje automático y su relevancia en el mundo actual. Discute cómo la tecnología ha evolucionado a lo largo del tiempo y qué impacto han tenido en la sociedad. autor subraya la necesidad de que las personas desarrollen un paradigma personal para percibir los avances tecnológicos, especialmente en el campo de la inteligencia artificial (IA). Este paradigma debe basarse en la supervivencia de la humanidad y en la unificación de los seres humanos en un Estado en guerra. A continuación, el autor profundiza en el proceso de desarrollo del conocimiento moderno necesario para utilizar el poder de la IA. Sostienen que este proceso requiere una comprensión profunda de los principios fundamentales del aprendizaje automático, incluidas las redes neuronales, los árboles de decisión y los algoritmos de agrupamiento.
Scikit-learn in details deep understanding è un libro che esamina le sottilità degli algoritmi di apprendimento automatico e le loro applicazioni in diversi ambiti. Il libro approfondisce gli aspetti tecnici di questi algoritmi, fornendo ai lettori una comprensione completa dell'oggetto. Include argomenti quali la pre-elaborazione dei dati, la scelta delle funzioni, la valutazione del modello e la configurazione di iperparametri e altri. L'autore sottolinea l'importanza di imparare i principi fondamentali dell'apprendimento automatico per utilizzare efficacemente questi metodi in scenari reali. Il libro inizia con l'introduzione dei concetti fondamentali dell'apprendimento automatico e della sua rilevanza nel mondo moderno. discute di come la tecnologia si sia evoluta nel tempo e dell'impatto che ha avuto sulla società. L'autore sottolinea la necessità per le persone di sviluppare un paradigma personale per la percezione dei progressi tecnologici, in particolare nell'intelligenza artificiale (IA). Questo paradigma deve basarsi sulla sopravvivenza dell'umanità e sull'unione delle persone in uno stato in guerra. Poi l'autore approfondisce il processo di sviluppo della conoscenza moderna necessaria per utilizzare la forza dell'IA. Sostengono che questo processo richiede una profonda comprensione dei principi fondamentali dell'apprendimento automatico, tra cui le reti neurali, gli alberi delle soluzioni e gli algoritmi di clusterizzazione.
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機械学習アルゴリズムの複雑さと様々な分野での応用を探求する本。本はこれらのアルゴリズムの技術的側面を掘り下げ、読者に主題の包括的な理解を提供します。データ前処理、フィーチャー選択、モデル評価、ハイパーパラメータの設定などのトピックをカバーしています。実際のシナリオでこれらの方法を効果的に使用するために、機械学習の基本原則を習得することの重要性を強調しています。この本は、機械学習の基本的な概念と現代世界におけるその関連性の導入から始まります。テクノロジーがどのように進化してきたか、それが社会に与える影響について解説します。著者は、特に人工知能(AI)において、人間が技術の進歩を知覚するための個人的なパラダイムを開発する必要性を強調している。このパラダイムは、人類の存続と人々の戦争状態への統一に基づいている必要があります。次に著者は、AIの力を活用するために必要な現代の知識を開発するプロセスを掘り下げます。彼らは、このプロセスにはニューラルネットワーク、意思決定木、クラスタリングアルゴリズムなどの機械学習の基本原理を深く理解する必要があると主張している。

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