Shivananda v (52 resultados)

Soft cover. Condición: Very Good. 96 pp., illustrations, map.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Paperback. Condición: New. This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations.By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms.What You Will LearnUnderstand and implement different recommender systems techniques with PythonEmploy popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filteringLeverage machine learning, NLP, and deep learning for building recommender systemsWho This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.…

paperback. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: New. This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations.By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms.What You Will LearnUnderstand and implement different recommender systems techniques with PythonEmploy popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filteringLeverage machine learning, NLP, and deep learning for building recommender systemsWho This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.…

Condición: New.

Condición: New. pp. xvi + 276.

Condición: New. pp. xiv + 126.

Condición: New. pp. xvi + 276.

Condición: New. pp. xiv + 126.

Condición: New. pp. xvi + 276.

Condición: New. pp. xiv + 126.

paperback. Condición: New. Brand new book, sourced directly from publisher. Dispatch time is 3-4 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condición: New. 2022. 1st ed. paperback. . . . . .

Paperback. Condición: Brand New. 190 pages. 9.25x6.10x0.43 inches. In Stock.

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Condición: New. 2022. 1st ed. paperback. . . . . . Books ship from the US and Ireland.

Hardcover. Condición: New. ISBN:9789383221530.

Condición: New.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New. In English.

Paperback. Condición: Brand New. 261 pages. 10.00x7.01x0.55 inches. In Stock.

Hardcover. Condición: New. 1st Edition. Contents: Foreword. Preface. 1. Introduction. 2. Archaeological Excavations in Gujarat. 3. Conclusion. Appendices. Bibliography. Index. This book 'Excavations in Gujarat' gives a comprehensive history of excavated sites in Gujarat along with the material culture unearthed during excavations. These are otherwise unpublished and a many lies in the doctoral thesis of scholars that are unavailable to others. Gujarat is the only state in India where the archaeological vestiges has revealed the evidences for the first, second and third urbanizations. Besides, the study of the variety of small objects from excavation has helped in understanding the life of the common people which is of enduring interest. All this had added to our knowledge and understanding of the development of the personality of India. (jacket).…

Condición: New.

Condición: New. In English.