Data Science Menggunakan Bahasa R Deep Learning

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Data Science Menggunakan Bahasa R Deep Learning

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Author : Dr. Adi Nugroho, S.T., M.M.S.I.
language : id
Publisher: Penerbit Andi
Release Date : 2024-10-25

Data Science Menggunakan Bahasa R Deep Learning written by Dr. Adi Nugroho, S.T., M.M.S.I. and has been published by Penerbit Andi this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-25 with Computers categories.

Data Science merupakan suatu biclang ilmu yang (relatif) baru di Indonesia, tujuan utamanya adalah memahami dan menganalisis segala fenomena yang berkaitan dengan data yang bertipe terstruktur, semiterstruktur, serta tidak terstruktur, yang datang dari berbagai sumber data/data source yang sangat beragam (meskipun saat ini sumber data yang terutama berasal dari Big Data dan loT/lnternet of Things) menggunakan pengetahuan yang terutama matematika, statistika, ilmu informasi (information science), serta ilmu komputer (terutama kecerdasan buatan/AI-Artificial Intelligence). McKinsey & Company pada 2018 melakukan survei tentang berapa sesungguhnya kebutuhan Data Scientist di Amerika Serikat pada tahun itu dan hasilnya menunjukkan bahwa dibutuhkan sekitar 140.000-190.000 Data Scientist (peningkatan sekitar 6,5 kali lipat dibandingkan tahun sebelumnya). Dalam hal ini kita juga bisa memperkirakan bahwa kebutuhan Data Scientist di Indonesia dalam beberapa tahun mendatang juga akan sangat tinggi (terutama jika kita mengingat salah satu kebijakan ekonomi pemerintah Indonesia saat ini, yang pada tahun-tahun mendatang akan lebih fokus pada berbagai bentuk perekonomian digital, seperti perdagangan barang dan jasa melalui jaringan internet atau e-commerce). Dengan demikian, buku yang berjudul Data Science Menggunakan Bahasa R: Deep Learning ini diharapkan akan memberi pemahaman tentang beberapa metode Data Science/Deep Learning yang paling sering digunakan oleh seorang Data Scientist untuk melakukan berbagai analisis data (data analytics) kepada kalangan akademisi, para mahasiswa di jurusan-jurusan yang terkait dengan ilmu komputer atau informatika, dan pada para praktisi di seluruh Indonesia.

Data Science Menggunakan Bahasa R

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Author : Dr. Adi Nugroho, ST, MMSI
language : id
Publisher: Penerbit Andi
Release Date : 2024-05-15

Data Science Menggunakan Bahasa R written by Dr. Adi Nugroho, ST, MMSI and has been published by Penerbit Andi this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-15 with Computers categories.

Data Science merupakan suatu bidang ilmu yang (relatif) baru di Indonesia yang tujuan utamanya adalah "memahami dan menganalisis segala fenomena yang berkaitan dengan data yang bertipe terstruktur, semi terstruktur, serta tidak terstruktur", yang datang dari berbagai sumber data/datasource yangsangat beragam (meskipun saat ini sumber data yang terutama berasal dari Big Data dan/atau loT/lnternet of Things) menggunakan pengetahuan (yang terutama) Matematika, Statistika, Ilmu Informasi (Information Science), serta Ilmu Komputer (terutama Kecerdasan Buatan/AI-Artificial Intelligence). McKinsey & Company pada tahun 2018 melakukan survei tentang berapa sesungguhnya kebutuhan Data Scientist di Amerika Serikat pada tahun itu dan hasilnya menunjukkan bahwa dibutuhkan sekitar 140.000-190.000 Data Scientist (peningkatan sekitar 6,5 kali lipat dibandingkan tahun sebelumnya). Dalam hal ini, kita juga bisa memperkirakan bahwa kebutuhan Data Scientist di Indonesia dalam beberapa tahun mendatang juga akan sangat tinggi (terutama jika kita mengingat salah satu kebijakan ekonomi pemerintah Indonesia saat ini yang pada tahun- tahun mendatang akan lebih fokus pada berbagai bentuk perekonomian digital seperti (contohnya) perdagangan barang dan jasa melalui jaringan Internet [e-Commerce]). Dengan demikian, buku yang berjudul Data Science Menggunakan Bahasa R: Analisis Data, Visualisasi, serta Pemodelan ini diharapkan akan memberi pemahaman tentang beberapa metode Data Science yang paling sering digunakan oleh seorang Data Scientist untuk melakukan berbagai analisis data (data analytics) kepada kalangan akademisi para mahasiswa di jurusan-jurusan yang terkait dengan Ilmu Komputer dan/atau Informatika, dan pada para praktisi di seluruh Indonesia.

Dasar Dasar Data Science Dan Aplikasinya Dengan Python

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Author : Wardana
language : id
Publisher: wawasan Ilmu
Release Date :

Dasar Dasar Data Science Dan Aplikasinya Dengan Python written by Wardana and has been published by wawasan Ilmu this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

Arus informasi dan teknologi yang semakin cepat sekarang ini, telah membawa banyak perubahan bagi kita sebagai masyarakat dunia, salah satu hasil teknologi adalah adanya software-software yang sangat membantu pekerjaan dan rutinitas kita sehari-hari, termasuk untuk pencarian dan pengolahan data dari berbagai sumber digital. Disini penulis mengetengahkan teori dasar tentang data science, tahapan pengolahan data dan apa saja yang perlu dikuasai untuk menjadi ilmuwan data, termasuk bahasa pemprogram data scince yang paling populer, yaitu bahasa Python. Pembaca akan belajar bagaimana membuat code python untuk menghasilkan diagram, menghitung statistik, membuat peramalan dan clustering data menggunakan kode python baik secara offline maupun online (menggunakan google colab). Dengan adanya teori dasar dan penerapannya dengan python, peluang pembaca untuk menjadi ilmuwan data yang sukses akan semakin besar. Penyajian materi diberikan secara jelas dan terperinci disertai dengan berbagai contoh kasus nyata sehari-hari. Dalam setiap bab diberikan contoh-contoh latihan dan diakhiri dengan soal latihan yang dapat membantu Pembaca untuk lebih memahami ulasan yang telah disajikan.

Belajar Bahasa R Metode Numerik

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Author : Iwan Sofana & Musthofa Lutfi
language : id
Publisher: Ojolinux
Release Date : 2021-12-03

Belajar Bahasa R Metode Numerik written by Iwan Sofana & Musthofa Lutfi and has been published by Ojolinux this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-03 with Computers categories.

Bahasa R sudah menjadi salah satu bahasa standar yang digunakan oleh Data Scientist, Ahli Statistika, Pakar Machine Learning, dan Ilmuwan. Sehingga kebutuhan tenaga kerja dan ketersediaan lapangan kerja yang berhubungan dengan aplikasi R semakin terbuka lebar. Saat ini masih belum banyak orang memanfaatkan R sebagai sarana untuk belajar metode numerik. Bahasa R lebih dikenal sebagai bahasa untuk data science dan machine learning. Orang lebih banyak menggunakan Matlab atau bahasa C untuk membuat program numerik. Padahal R menyediakan segudang fasilitas untuk keperluan numerical analysis yang tidak kalah bagus dibandingkan Matlab dan bahasa lainnya. Malah boleh dibilang sudah sangat lengkap. Beberapa pokok bahasan dan metode numerik yang dikupas dalam buku ini antara lain: Sintaks dan konsep bahasa R. Mencari akar persamaan dengan metode Nwton, Bisection, Secant, dan sebagainya. Menghitung integral numerik dengan Simpson, Riemann, Boole, dan lain-lain. Persamaan diferensial biasa dan parsial. Sistem persamaan linier dan non linier, interpolasi dan ekstrapolasi, Particle Swamp Optimization, plot grafik 2D dan 3D.

Pengantar Data Science

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Author : Fatwa Ramdani
language : id
Publisher: Bumi Aksara
Release Date : 2024-01-09

Pengantar Data Science written by Fatwa Ramdani and has been published by Bumi Aksara this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-09 with Computers categories.

Perkembangan dunia komputasi dan komunikasi menghasilkan masyarakat yang bergantung kepada informasi. Namun, sebagian besar informasi masih dalam bentuk mentahnya yaitu berupa data. Jika data didefinisikan sebagai fakta yang direkam, maka informasi adalah seperangkat pola atau harapan yang berdasarkan dari data tersebut. Ada banyak sekali informasi yang masih tersembunyi dari suatu kumpulan data yang cukup besar. Informasi ini menyimpan nilai yang berharga tetapi belum diartikulasikan. Data Science adalah bidang ilmu yang berkembang dan bergerak cepat. Buku ini menjelaskan konsep fundamental dari data science sehingga para pembaca dapat memahami inti dari bidang ilmu tersebut. Buku ini dirancang untuk pembaca umum dalam rangka menjelaskan ide-ide dasar dari data science, awal kemunculan, sampai dengan pembahasan yang komprehensif tentang model data science dan penggunaannya di kehidupan sehari-hari. Tujuan dari buku ini adalah untuk memperkenalkan kepada pembaca tentang bidang ilmu data science, memahami potensi dan dampaknya terhadap pengambilan keputusan. Misi kami adalah untuk membantu para pembaca mewujudkannya melalui pengetahuan bidang ilmu data science. Pembaca diharapkan dapat memanfaatkan kumpulan data yang relevan untuk membuat keputusan yang cepat dan tepat.

Real World Machine Learning

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Author : Henrik Brink
language : en
Publisher: Simon and Schuster
Release Date : 2016-09-15

Real World Machine Learning written by Henrik Brink and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-15 with Computers categories.

Summary Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning systems help you find valuable insights and patterns in data, which you'd never recognize with traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior, and make fact-based recommendations. It's a hot and growing field, and up-to-speed ML developers are in demand. About the Book Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling, classification, and regression. You'll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you're done, you'll be ready to successfully build, deploy, and maintain your own powerful ML systems. What's Inside Predicting future behavior Performance evaluation and optimization Analyzing sentiment and making recommendations About the Reader No prior machine learning experience assumed. Readers should know Python. About the Authors Henrik Brink, Joseph Richards and Mark Fetherolf are experienced data scientists engaged in the daily practice of machine learning. Table of Contents PART 1: THE MACHINE-LEARNING WORKFLOW What is machine learning? Real-world data Modeling and prediction Model evaluation and optimization Basic feature engineering PART 2: PRACTICAL APPLICATION Example: NYC taxi data Advanced feature engineering Advanced NLP example: movie review sentiment Scaling machine-learning workflows Example: digital display advertising

Programming Pytorch For Deep Learning

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Author : Ian Pointer
language : en
Publisher: O'Reilly Media
Release Date : 2019-09-20

Programming Pytorch For Deep Learning written by Ian Pointer and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-20 with Computers categories.

Take the next steps toward mastering deep learning, the machine learning method that’s transforming the world around us by the second. In this practical book, you’ll get up to speed on key ideas using Facebook’s open source PyTorch framework and gain the latest skills you need to create your very own neural networks. Ian Pointer shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text,and more through deep dives into each element. He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production. Learn how to deploy deep learning models to production Explore PyTorch use cases from several leading companies Learn how to apply transfer learning to images Apply cutting-edge NLP techniques using a model trained on Wikipedia Use PyTorch’s torchaudio library to classify audio data with a convolutional-based model Debug PyTorch models using TensorBoard and flame graphs Deploy PyTorch applications in production in Docker containers and Kubernetes clusters running on Google Cloud

Deep Learning Made Easy With R

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Author : N. D. Lewis
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-01-10

Deep Learning Made Easy With R written by N. D. Lewis and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-10 with categories.

Master Deep Learning with this fun, practical, hands on guide. With the explosion of big data deep learning is now on the radar. Large companies such as Google, Microsoft, and Facebook have taken notice, and are actively growing in-house deep learning teams. Other large corporations are quickly building out their own teams. If you want to join the ranks of today's top data scientists take advantage of this valuable book. It will help you get started. It reveals how deep learning models work, and takes you under the hood with an easy to follow process showing you how to build them faster than you imagined possible using the powerful, free R predictive analytics package. Bestselling decision scientist Dr. N.D Lewis shows you the shortcut up the steep steps to the very top. It's easier than you think. Through a simple to follow process you will learn how to build the most successful deep learning models used for learning from data. Once you have mastered the process, it will be easy for you to translate your knowledge into your own powerful applications. If you want to accelerate your progress, discover the best in deep learning and act on what you have learned, this book is the place to get started. YOU'LL LEARN HOW TO: Understand Deep Neural Networks Use Autoencoders Unleash the power of Stacked Autoencoders Leverage the Restricted Boltzmann Machine Develop Recurrent Neural Networks Master Deep Belief Networks Everything you need to get started is contained within this book. It is your detailed, practical, tactical hands on guide - the ultimate cheat sheet for deep learning mastery. A book for everyone interested in machine learning, predictive analytic techniques, neural networks and decision science. Start building smarter models today using R! Buy the book today. Your next big breakthrough using deep learning is only a page away!

Data Mining

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Author : Krzysztof J. Cios
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-10-05

Data Mining written by Krzysztof J. Cios 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 2007-10-05 with Computers categories.

“If you torture the data long enough, Nature will confess,” said 1991 Nobel-winning economist Ronald Coase. The statement is still true. However, achieving this lofty goal is not easy. First, “long enough” may, in practice, be “too long” in many applications and thus unacceptable. Second, to get “confession” from large data sets one needs to use state-of-the-art “torturing” tools. Third, Nature is very stubborn — not yielding easily or unwilling to reveal its secrets at all. Fortunately, while being aware of the above facts, the reader (a data miner) will find several efficient data mining tools described in this excellent book. The book discusses various issues connecting the whole spectrum of approaches, methods, techniques and algorithms falling under the umbrella of data mining. It starts with data understanding and preprocessing, then goes through a set of methods for supervised and unsupervised learning, and concludes with model assessment, data security and privacy issues. It is this specific approach of using the knowledge discovery process that makes this book a rare one indeed, and thus an indispensable addition to many other books on data mining. To be more precise, this is a book on knowledge discovery from data. As for the data sets, the easy-to-make statement is that there is no part of modern human activity left untouched by both the need and the desire to collect data. The consequence of such a state of affairs is obvious.

The Global Findex Database 2017

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Author : Asli Demirguc-Kunt
language : en
Publisher: World Bank Publications
Release Date : 2018-04-19

The Global Findex Database 2017 written by Asli Demirguc-Kunt and has been published by World Bank Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-19 with Business & Economics categories.

In 2011 the World Bank—with funding from the Bill and Melinda Gates Foundation—launched the Global Findex database, the world's most comprehensive data set on how adults save, borrow, make payments, and manage risk. Drawing on survey data collected in collaboration with Gallup, Inc., the Global Findex database covers more than 140 economies around the world. The initial survey round was followed by a second one in 2014 and by a third in 2017. Compiled using nationally representative surveys of more than 150,000 adults age 15 and above in over 140 economies, The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution includes updated indicators on access to and use of formal and informal financial services. It has additional data on the use of financial technology (or fintech), including the use of mobile phones and the Internet to conduct financial transactions. The data reveal opportunities to expand access to financial services among people who do not have an account—the unbanked—as well as to promote greater use of digital financial services among those who do have an account. The Global Findex database has become a mainstay of global efforts to promote financial inclusion. In addition to being widely cited by scholars and development practitioners, Global Findex data are used to track progress toward the World Bank goal of Universal Financial Access by 2020 and the United Nations Sustainable Development Goals.The database, the full text of the report, and the underlying country-level data for all figures—along with the questionnaire, the survey methodology, and other relevant materials—are available at www.worldbank.org/globalfindex.