Machine Learning Dengan R Teori Praktikum
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Machine Learning Dengan R Teori Praktikum
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Author : Joko Ade Nursiyono
language : id
Publisher: Media Nusa Creative (MNC Publishing)
Release Date : 2023-03-02
Machine Learning Dengan R Teori Praktikum written by Joko Ade Nursiyono and has been published by Media Nusa Creative (MNC Publishing) this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-02 with Education categories.
Machine Learning (ML) merupakan cabang ilmu di era Data Science dan Big Data yang akhir-akhir ini populer. Dengan segala manfaat yang diberikan oleh ML, banyak aspek dalam kehidupan sehari- hari yang seketika berubah menjadi sebuah sistem otomatis yang berjalan menggunakan beragam jenis algoritma pemrograman mesin.
Pengantar Analisis Data Dengan R Studio
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Author : Akbar Iskandar
language : id
Publisher: Yayasan Cendekiawan Inovasi Digital Indonesia
Release Date : 2023-06-23
Pengantar Analisis Data Dengan R Studio written by Akbar Iskandar and has been published by Yayasan Cendekiawan Inovasi Digital Indonesia this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-23 with Mathematics categories.
Machine Learning With Python
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Author : Abhishek Vijayvargia
language : en
Publisher: BPB Publications
Release Date : 2018-03-01
Machine Learning With Python written by Abhishek Vijayvargia and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-01 with Computers categories.
Providing code examples in python, this book introduces the concepts of machine learning with mathematical explanations and programming fundamentals. --
Bookdown
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Author : Yihui Xie
language : en
Publisher: CRC Press
Release Date : 2016-12-12
Bookdown written by Yihui Xie and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-12 with Mathematics categories.
bookdown: Authoring Books and Technical Documents with R Markdown presents a much easier way to write books and technical publications than traditional tools such as LaTeX and Word. The bookdown package inherits the simplicity of syntax and flexibility for data analysis from R Markdown, and extends R Markdown for technical writing, so that you can make better use of document elements such as figures, tables, equations, theorems, citations, and references. Similar to LaTeX, you can number and cross-reference these elements with bookdown. Your document can even include live examples so readers can interact with them while reading the book. The book can be rendered to multiple output formats, including LaTeX/PDF, HTML, EPUB, and Word, thus making it easy to put your documents online. The style and theme of these output formats can be customized. We used books and R primarily for examples in this book, but bookdown is not only for books or R. Most features introduced in this book also apply to other types of publications: journal papers, reports, dissertations, course handouts, study notes, and even novels. You do not have to use R, either. Other choices of computing languages include Python, C, C++, SQL, Bash, Stan, JavaScript, and so on, although R is best supported. You can also leave out computing, for example, to write a fiction. This book itself is an example of publishing with bookdown and R Markdown, and its source is fully available on GitHub.
Interpretable Machine Learning With Python
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Author : Serg Masís
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-03-26
Interpretable Machine Learning With Python written by Serg Masís and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-26 with Computers categories.
A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with interpretability techniques to build fairer, safer, and more reliable models Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models Book DescriptionDo you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning. What you will learn Recognize the importance of interpretability in business Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes Become well-versed in interpreting models with model-agnostic methods Visualize how an image classifier works and what it learns Understand how to mitigate the influence of bias in datasets Discover how to make models more reliable with adversarial robustness Use monotonic constraints to make fairer and safer models Who this book is for This book is primarily written for data scientists, machine learning developers, and data stewards who find themselves under increasing pressures to explain the workings of AI systems, their impacts on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a solid grasp on the Python programming language and ML fundamentals is needed to follow along.
E Learning Strategies For Delivering Knowledge In The Digital Age
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Author : Marc J. Rosenberg
language : en
Publisher: McGraw Hill Professional
Release Date : 2000-11-16
E Learning Strategies For Delivering Knowledge In The Digital Age written by Marc J. Rosenberg and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-11-16 with Architecture categories.
Internet and intranet technologies offer tremendous opportunities to bring learning into the mainstream of business. E-Learning outlines how to develop an organization-wide learning strategy based on cutting-edge technologies and explains the dramatic strategic, organizational, and technology issues involved. Written for professionals responsible for leading the revolution in workplace learning, E-Learning takes a broad, strategic perspective on corporate learning. This wake-up call for executives everywhere discusses: • Requirements for building a viable e-learning strategy • How online learning will change the nature of training organizations • Knowledge management and other new forms of e-learning Marc J. Rosenberg, Ph.D. (Hillsborough, NJ) is an independent consultant specializing in knowledge management, e-learning strategy and the reinvention of training. Prior to this, he was a senior direction and kowledge management field leader for consulting firm DiamondCluster International.
Evolutionary Machine Learning Techniques
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Author : Seyedali Mirjalili
language : en
Publisher: Springer Nature
Release Date : 2019-11-11
Evolutionary Machine Learning Techniques written by Seyedali Mirjalili and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-11 with Technology & Engineering categories.
This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.
Computer Vision And Image Processing
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Author :
language : en
Publisher:
Release Date : 2020
Computer Vision And Image Processing written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computer vision categories.
This two-volume set (CCIS 1147, CCIS 1148) constitutes the refereed proceedings of the 4th International Conference on Computer Vision and Image Processing. held in Jaipur, India, in September 2019. The 73 full papers and 10 short papers were carefully reviewed and selected from 202 submissions. The papers are organized according to the following topics: Part I: Biometrics; Computer Forensic; Computer Vision; Dimension Reduction; Healthcare Information Systems; Image Processing; Image segmentation; Information Retrieval; Instance based learning; Machine Learning. Part II: Neural Network; Object Detection; Object Recognition; Online Handwriting Recognition; Optical Character Recognition; Security and Privacy; Unsupervised Clustering.
Essentials Of Instructional Technology
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Author : A. R. Rather
language : en
Publisher: Discovery Publishing House
Release Date : 2004
Essentials Of Instructional Technology written by A. R. Rather and has been published by Discovery Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Education categories.
Contents: Models of Teaching, Teaching: Principles and Maxims, Audio-Visual Aids, Different Devices, Teaching by Simulation, Learning by Programme, Micro Teaching, Teaching Methods, Educational Objectives and Taxonomy, Planning the Lessons.
Computational Economics
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Author : David A. Kendrick
language : en
Publisher: Princeton University Press
Release Date : 2011-10-23
Computational Economics written by David A. Kendrick and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-23 with Business & Economics categories.
The ability to conceptualize an economic problem verbally, to formulate it as a mathematical model, and then represent the mathematics in software so that the model can be solved on a computer is a crucial skill for economists. Computational Economics contains well-known models--and some brand-new ones--designed to help students move from verbal to mathematical to computational representations in economic modeling. The authors' focus, however, is not just on solving the models, but also on developing the ability to modify them to reflect one's interest and point of view. The result is a book that enables students to be creative in developing models that are relevant to the economic problems of their times. Unlike other computational economics textbooks, this book is organized around economic topics, among them macroeconomics, microeconomics, and finance. The authors employ various software systems--including MATLAB, Mathematica, GAMS, the nonlinear programming solver in Excel, and the database systems in Access--to enable students to use the most advantageous system. The book progresses from relatively simple models to more complex ones, and includes appendices on the ins and outs of running each program. The book is intended for use by advanced undergraduates and professional economists and even, as a first exposure to computational economics, by graduate students. Organized by economic topics Progresses from simple to more complex models Includes instructions on numerous software systems Encourages customization and creativity