Pengenalan Praktis Supervised Machine Learning Dengan Jupyter Notebook
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Pengenalan Praktis Supervised Machine Learning Dengan Jupyter Notebook
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Author : Yudha Nurdin, Khairun Saddami, Nasaruddin
language : id
Publisher: USK Press
Release Date : 2025-03-26
Pengenalan Praktis Supervised Machine Learning Dengan Jupyter Notebook written by Yudha Nurdin, Khairun Saddami, Nasaruddin and has been published by USK Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-26 with Education categories.
Buku ajar ini disusun sebagai pendukung beberapa mata kuliah di antaranya Kecerdasan Artifisial, Machine Learning, dan Deep Learning untuk memberikan pemahaman dasar bagi mahasiswa/i tentang teknik pembelajaran mesin terawasi menggunakan Jupyter Notebook. Supervised machine learning atau pembelajaran mesin terawasi merupakan salah satu metode yang banyak digunakan dalam berbagai aplikasi, mulai dari analisis data hingga pengembangan kecerdasan artifisial. Dengan Jupyter Notebook, pengguna dapat memanfaatkan lingkungan interaktif untuk mengembangkan dan mengeksplorasi model machine learning dengan mudah dan efisien. Buku ajar ini diawali dengan konsep dasar supervised machine learning, gambaran praktis hingga penerapannya menggunakan Jupyter Notebook. Dalam pembahasannya, kami menyajikan contoh-contoh sederhana yang dapat diikuti oleh pemula untuk memulai eksplorasi lebih lanjut dalam bidang ini. Kami berharap buku ini dapat memberikan manfaat bagi pembaca yang tertarik dalam dunia pembelajaran mesin, khususnya yang ingin memahami dasar-dasar penggunaan Jupyter Notebook dalam implementasi model-model supervised machine learning.
Pengenalan Machine Learning Menggunakan Jupyter Notebook
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Author : Imam Tahyudin
language : id
Publisher: Zahira Media Publisher
Release Date : 2020-06-01
Pengenalan Machine Learning Menggunakan Jupyter Notebook written by Imam Tahyudin and has been published by Zahira Media Publisher this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-01 with Computers categories.
Buku ini dibuka dengan pembahasan tentang pengenalan Machine Learning, perbedaan Machine Learning dengan AI, dan deep learning. Selanjutnya dibahas berbagai metode yang digunakan dalam Machine Learning seperti decision tree, random forest, SVM, naïve bayes, KNN, regresi linier, regresi logistic, K-Means, dan Apriori. Buku ini menarik untuk dipelajari karena dijelaskan secara sederhana dan detail. Selain itu buku ini dilengkapi dengan implementasi menggunakan yupiter notebook. Impelemntasi ini mudah untuk diikuti karena dijelaskan secara bertahap menggunakan software open source yang ringan dan powerfull.
Data Lake Architecture
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Author : Bill Inmon
language : en
Publisher:
Release Date : 2016
Data Lake Architecture written by Bill Inmon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Big data categories.
Data Lake Architecture will explain how to build a useful data lake, where data scientists and data analysts can solve business challenges and identify new business opportunities
Introduction To Artificial Intelligence
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Author : Mariusz Flasiński
language : en
Publisher: Springer
Release Date : 2016-08-31
Introduction To Artificial Intelligence written by Mariusz Flasiński and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-31 with Computers categories.
In the chapters in Part I of this textbook the author introduces the fundamental ideas of artificial intelligence and computational intelligence. In Part II he explains key AI methods such as search, evolutionary computing, logic-based reasoning, knowledge representation, rule-based systems, pattern recognition, neural networks, and cognitive architectures. Finally, in Part III, he expands the context to discuss theories of intelligence in philosophy and psychology, key applications of AI systems, and the likely future of artificial intelligence. A key feature of the author's approach is historical and biographical footnotes, stressing the multidisciplinary character of the field and its pioneers. The book is appropriate for advanced undergraduate and graduate courses in computer science, engineering, and other applied sciences, and the appendices offer short formal, mathematical models and notes to support the reader.
Deep Learning With Applications Using Python
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Author : Navin Kumar Manaswi
language : en
Publisher: Apress
Release Date : 2018-04-04
Deep Learning With Applications Using Python written by Navin Kumar Manaswi and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-04 with Computers categories.
Explore deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning. This book covers convolutional neural networks, recurrent neural networks, and multilayer perceptrons. It also discusses popular APIs such as IBM Watson, Microsoft Azure, and scikit-learn. What You Will Learn Work with various deep learning frameworks such as TensorFlow, Keras, and scikit-learn. Use face recognition and face detection capabilities Create speech-to-text and text-to-speech functionality Engage with chatbots using deep learning Who This Book Is For Data scientists and developers who want to adapt and build deep learning applications.
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.
All Of Statistics
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Author : Larry Wasserman
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-12-11
All Of Statistics written by Larry Wasserman and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-11 with Mathematics categories.
Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
Introduction To Data Science
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Author : Rafael A. Irizarry
language : en
Publisher: CRC Press
Release Date : 2019-11-12
Introduction To Data Science written by Rafael A. Irizarry and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-12 with Mathematics categories.
Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. A complete solutions manual is available to registered instructors who require the text for a course.
Expert Systems And Probabilistic Network Models
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Author : Enrique Castillo
language : en
Publisher: Springer Science & Business Media
Release Date : 1996-12-13
Expert Systems And Probabilistic Network Models written by Enrique Castillo and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-12-13 with Computers categories.
Artificial intelligence and expert systems have seen a great deal of research in recent years, much of which has been devoted to methods for incorporating uncertainty into models. This book is devoted to providing a thorough and up-to-date survey of this field for researchers and students.
Deep Learning For Computer Vision
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Author : Rajalingappaa Shanmugamani
language : en
Publisher: Packt Publishing
Release Date : 2018-01-23
Deep Learning For Computer Vision written by Rajalingappaa Shanmugamani and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-23 with Computers categories.
Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks Key Features Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more Includes tips on optimizing and improving the performance of your models under various constraints Book Description Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation. What you will learn Set up an environment for deep learning with Python, TensorFlow, and Keras Define and train a model for image and video classification Use features from a pre-trained Convolutional Neural Network model for image retrieval Understand and implement object detection using the real-world Pedestrian Detection scenario Learn about various problems in image captioning and how to overcome them by training images and text together Implement similarity matching and train a model for face recognition Understand the concept of generative models and use them for image generation Deploy your deep learning models and optimize them for high performance Who this book is for This book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python--and some understanding of machine learning concepts--is required to get the best out of this book.