Introduction to Forestry Science, Iml PDF Download
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Author: John Burton
Publisher:
ISBN: 9781418030889
Category :
Languages : en
Pages :
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Book Description
Author: John Burton
Publisher:
ISBN: 9781418030889
Category :
Languages : en
Pages :
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Book Description
Author: L. DeVere Burton
Publisher: Cengage Learning
ISBN: 9780827380103
Category : Forest management
Languages : en
Pages : 0
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Book Description
A senior high textbook focusing on the North American forest regions; classification, anatomy, and diseases of trees; forest management and products; and urban forestry.
Author: Christoph Molnar
Publisher: Lulu.com
ISBN: 0244768528
Category : Computers
Languages : en
Pages : 320
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Book Description
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Author:
Publisher:
ISBN:
Category : Outdoor recreation
Languages : en
Pages : 628
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Book Description
Author: L. De vere Burton
Publisher:
ISBN: 9789353500405
Category :
Languages : en
Pages : 571
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Book Description
Author: L. Burton
Publisher: Cengage Learning
ISBN: 9781418030872
Category : Science
Languages : en
Pages : 512
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Book Description
Introduction to Forestry Science, Second Edition, is an introductory forestry book that covers the principles and practices of forest management that are commonly practiced in the United States. Processing of wood and forest products is an important unit that points out the economic impact of forests on the U.S. economy. A complete chapter focuses on government historical events and policies that have influenced forest management practices since North America was colonized. Attention is given to regional differences in forests and forest management. Laws and regulations that govern the use of forests are also addressed. This text follows an applied science approach that integrates science principles with forestry practices. Science topics are woven throughout, and reinforced through the student s need to know feature. Topics covered include cell structure and function, cell growth and reproduction, elemental cycles, principles of ecology, the food pyramid, plant anatomy and physiology, entomology, water quality, air quality and preservation of endangered species. In addition, career options related to forests and forest management are explored. Other features include terms for understanding, student-learning objectives, chapter summaries, review questions, learning activities, and profiles on events and issues related to forests and science. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
Author: United States. Forest Service. Northern Research Station
Publisher:
ISBN:
Category : Forests and forestry
Languages : en
Pages : 632
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Book Description
Author:
Publisher:
ISBN:
Category : Forests and forestry
Languages : en
Pages : 476
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Book Description
Author: Renee Peugh
Publisher: Delmar
ISBN: 9781111308414
Category : Reference
Languages : en
Pages : 161
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Book Description
The Laboratory Manual is a valuable tool designed to enhance your understanding of the concepts discussed in the core textbook. Each chapter begins wth a list of objectives, followed by activities designed to achieve the objectives, and concludes with a list of references for further study.
Author: Brad Boehmke
Publisher: CRC Press
ISBN: 1000730433
Category : Business & Economics
Languages : en
Pages : 373
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Book Description
Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.
