Deep Learning Citra Medis Berbasis Pemrograman Python
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Deep Learning Citra Medis Berbasis Pemrograman Python
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Author : Mada Sanjaya W. S., Ph.D.
language : id
Publisher: Bolabot
Release Date : 2023-08-31
Deep Learning Citra Medis Berbasis Pemrograman Python written by Mada Sanjaya W. S., Ph.D. and has been published by Bolabot this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-31 with Computers categories.
Dalam era modern ini, citra medis telah menjadi sumber data yang sangat penting dalam diagnosis penyakit. Namun, memahami, menganalisis, dan menginterpretasikan citra medis secara manual dapat menjadi tugas yang rumit dan memakan waktu. Inilah mengapa deep learning, dengan kemampuannya dalam mempelajari pola-pola kompleks dari data, menjadi sangat relevan dalam bidang citra medis. Buku ini dirancang untuk membantu pembaca memahami konsep dasar deep learning serta mengimplementasikannya dalam pemrograman Python. Kami akan membahas berbagai topik yang diperlukan untuk memulai perjalanan Anda dalam menerapkan deep learning pada citra medis. Pada bagian awal buku, kita akan menjelajahi dasar-dasar deep learning, termasuk jaringan saraf tiruan (neural network) dan arsitektur dasar seperti Convolutional Neural Network (CNN). Selanjutnya, kami akan melangkah ke tahap pemrosesan citra, termasuk teknik pra-pemrosesan yang diperlukan untuk meningkatkan kualitas citra medis. Setelah mempelajari dasar-dasar, kita akan beralih ke aplikasi praktis deep learning pada citra medis. Kami akan menggunakan pemrograman Python dan library seperti TensorFlow dan Keras untuk membangun dan melatih model deep learning. Beberapa aplikasi yang akan kita bahas meliputi prediksi jenis tumor otak dari citra MRI, prediksi penyakit dari citra CT scan ginjal, serta prediksi COVID-19 dari citra chest x-ray dan chest CT scan. Kami juga akan membahas prediksi tuberculosis dari citra chest x-ray, serta prediksi dan segmentasi kanker payudara dari citra ultrasound. Selama Anda menjelajahi buku ini, kami harap Anda dapat mengembangkan pemahaman yang mendalam tentang deep learning dalam citra medis dan mampu mengimplementasikannya dalam proyek-proyek nyata. Dengan memadukan pengetahuan ilmiah dengan kekuatan pemrograman Python, kita dapat meningkatkan diagnosis dan pengobatan penyakit secara signifikan.
Fisika Komputasi Berbasis Machine Learning Dengan Pemrograman Python
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Author : Mada Sanjaya W. S., Ph.D.
language : id
Publisher: Bolabot
Release Date : 2024-12-31
Fisika Komputasi Berbasis Machine Learning Dengan Pemrograman Python written by Mada Sanjaya W. S., Ph.D. and has been published by Bolabot this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-31 with Computers categories.
Buku ini dibagi menjadi beberapa bab yang mencakup dasar-dasar fisika komputasi, konsep machine learning, implementasi Python, hingga aplikasi nyata seperti simulasi gerak partikel, optimasi sistem fisika, dan prediksi berbasis data. Setiap bab disusun dengan pendekatan yang terstruktur dan disertai dengan contoh implementasi program Python agar pembaca dapat memahami konsep secara praktis dan aplikatif.
Deep Learning Convolutional Neural Networks Pemrograman Python Arduino Serta Aplikasi Pada Komputer Vision Speech Recognition Dan Mobile Robot
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Author : Mada Sanjaya W. S., Ph.D.
language : id
Publisher: Bolabot
Release Date : 2024-06-30
Deep Learning Convolutional Neural Networks Pemrograman Python Arduino Serta Aplikasi Pada Komputer Vision Speech Recognition Dan Mobile Robot written by Mada Sanjaya W. S., Ph.D. and has been published by Bolabot this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-30 with Computers categories.
Sebagai bagian dari praktisi, kami memahami pentingnya kebutuhan akan sumber daya yang dapat memberikan pandangan mendalam dan pemahaman yang komprehensif tentang teknologi mutakhir. Dalam buku ini, kami berusaha untuk memberikan landasan yang kokoh dan aplikasi praktis dalam penggunaan CNN, yang telah menjadi salah satu alat utama dalam riset dan pengembangan di berbagai bidang, termasuk Komputer Vision, Speech Recognition, dan Navigasi Mobile Robot. Setiap bab dalam buku ini disusun dengan literatur yang relevan dan contoh kode yang terperinci. Kami percaya bahwa buku ini dapat menjadi referensi yang berharga bagi para peneliti, mahasiswa, dan praktisi yang tertarik dalam memahami dan mengaplikasikan konsep Deep Learning dalam konteks praktis. Dalam proses penyusunan buku ini, kami ingin mengucapkan terima kasih kepada semua pihak yang telah memberikan dukungan, inspirasi, dan bimbingan. Semoga buku ini dapat memberikan kontribusi yang berarti dalam upaya Anda untuk mengembangkan pengetahuan dan keterampilan dalam bidang pemrograman Deep Learning.
Kecerdasan Buatan Dengan Deep Computer Vision
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Author : Dr. Eng. Said Mirza Pahlevi
language : id
Publisher: Elex Media Komputindo
Release Date : 2024-10-09
Kecerdasan Buatan Dengan Deep Computer Vision written by Dr. Eng. Said Mirza Pahlevi and has been published by Elex Media Komputindo this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-09 with Computers categories.
Buku ini membahas tentang Deep Computer Vision yang merupakan cabang kecerdasan buatan yang memanfaatkan jaringan saraf tiruan untuk melakukan analisis dan pemrosesan gambar/citra/video secara real-time. Bagian Pertama buku membahas teknologi Computer Vision konvensional yang menggunakan algoritme Non-Deep Learning. Pada Bagian Kedua buku, pembahasan mengenai Computer Vision difokuskan pada tugas-tugas yang lebih kompleks dengan memanfaatkan teknologi Deep Learning. Tugas-tugas kompleks tersebut meliputi deteksi, penelusuran (tracking), segmentasi, dan prediksi pose objek serta klasifikasi pada gambar/citra/video secara real-time. Penulis memberikan pengantar langkah demi langkah ke konsep-konsep utama, dengan penjelasan yang gamblang dan terperinci tentang metode komputasi di balik setiap teknik. Buku ini juga menyertakan contoh-contoh praktis dalam kode pemrograman berbasis Bahasa Pemrograman Python yang menunjukkan bagaimana Deep Computer Vision dapat diterapkan pada masalah-masalah di dunia nyata, seperti pengenalan wajah seseorang, deteksi kendaraan pada webcam, dan menghitung tren kepadatan lalu-lintas. Kode program dijelaskan secara baris-per-baris sehingga memudahkan pembaca dalam pemahaman program serta melanjutkan pengembangan program tersebut untuk kasus-kasus spesifik yang dihadapi mereka. Secara rinci, isi buku mencakup pemahaman Deep Computer Vision pada: • Teori dan metode Computer Vision konvensional beserta pemrogramannya. • Teori, konsep dasar, dan pemrograman Deep Learning terutama Convolutional Neural Network (CNN). • Teori, konsep dasar, dan pemrograman teknologi prediksi objek terkini: You Only Look Once (YOLO). • Proyek-proyek Deep Computer Vision yang mempraktikan teknologi ini pada kasus-kasus di dunia nyata.
Python Untuk Analisis Data
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Author : Nabila Sava Nur Az-zahrani
language : id
Publisher: SIEGA Publisher
Release Date : 2025-07-01
Python Untuk Analisis Data written by Nabila Sava Nur Az-zahrani and has been published by SIEGA Publisher this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-01 with Computers categories.
Perkembangan teknologi informasi telah mengubah dunia akuntansi secara menyeluruh. Kini, akuntansi tidak lagi hanya berfokus pada pencatatan dan penyusunan laporan keuangan, tetapi juga memiliki peran strategis dalam menghasilkan insight berbasis data untuk mendukung pengambilan keputusan yang lebih akurat, cepat, dan bernilai. Di tengah arus transformasi digital ini, kemampuan untuk mengolah, menganalisis, dan menginterpretasikan data menjadi salah satu kompetensi kunci dalam profesi akuntansi. Kita hidup di era di mana data tersedia dalam jumlah yang besar dan terus bertambah setiap detik. Dalam konteks ilmiah, peran seorang akuntan juga ikut berubah, bukan hanya sebagai penyaji laporan, tetapi juga sebagai analisis data yang andal. Keterampilan dalam analisis data kini menjadi syarat penting bagi akuntan yang ingin tetap relevan dan berdaya saing di era digital. Salah satu alat bantu yang banyak digunakan dalam analisis data adalah Python, sebuah bahasa pemrograman yang dikenal luas karena fleksibilitasnya, kemudahan penggunaannya, serta kelengkapan pustakanya untuk keperluan statistik, analisis data, dan visualisasi. Dukungan terhadap platfrom seperti Google Collab juga menjadikan Python dapat diakses dengan mudah, bahkan oleh pemula sekalipun. Buku ini disusun sebagai panduan praktis dan aplikatif bagi mahasiswa, profesional muda, maupun siapa pun yang ingin memahami dasar-dasar analisis data menggunakan Python dalam konteks akuntansi digital. Materi dalam buku ini dirancang secara bertahap dan terstruktur, dimulai dari pemahaman mengenai pentingnya analisis data dalam akuntansi digital, pengenalan Python sebagai alat utama, hingga penerapan nyata melalui studi kasus komprehensif yang diambil dari dunia akuntansi digital. Melalui buku ini, diharapkan pembaca tidak hanya memperoleh pemahaman konseptual, tetapi juga mampu mengimplementasikan teknik analisis data secara langsung. Dengan keterampilan ini, pembaca akan lebih siap menghadapi tantangan dan tuntunan dunia akuntansi yang semakin berbasis data.
Discovering Knowledge In Data
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Author : Daniel T. Larose
language : en
Publisher: John Wiley & Sons
Release Date : 2005-01-28
Discovering Knowledge In Data written by Daniel T. Larose and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-01-28 with Computers categories.
Learn Data Mining by doing data mining Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets. Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include: * Data preprocessing and classification * Exploratory analysis * Decision trees * Neural and Kohonen networks * Hierarchical and k-means clustering * Association rules * Model evaluation techniques Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge. An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.
Applied Natural Language Processing With Python
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Author : Taweh Beysolow II
language : en
Publisher: Apress
Release Date : 2018-09-12
Applied Natural Language Processing With Python written by Taweh Beysolow II and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-12 with Computers categories.
Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. Applied Natural Language Processing with Python starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment. What You Will Learn Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim Manipulate and preprocess raw text data in formats such as .txt and .pdf Strengthen your skills in data science by learning both the theory and the application of various algorithms Who This Book Is For You should be at least a beginner in ML to get the most out of this text, but you needn’t feel that you need be an expert to understand the content.
Data Mining And Knowledge Discovery Handbook
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Author : Oded Maimon
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-05-28
Data Mining And Knowledge Discovery Handbook written by Oded Maimon 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 2006-05-28 with Computers categories.
Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.
Scientific Discovery
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Author : Pat Langley
language : en
Publisher: MIT Press
Release Date : 1987
Scientific Discovery written by Pat Langley and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987 with Computers categories.
Scientific discovery is often regarded as romantic and creative--and hence unanalyzable--whereas the everyday process of verifying discoveries is sober and more suited to analysis. Yet this fascinating exploration of how scientific work proceeds argues that however sudden the moment of discovery may seem, the discovery process can be described and modeled. Using the methods and concepts of contemporary information-processing psychology (or cognitive science) the authors develop a series of artificial-intelligence programs that can simulate the human thought processes used to discover scientific laws. The programs--BACON, DALTON, GLAUBER, and STAHL--are all largely data-driven, that is, when presented with series of chemical or physical measurements they search for uniformities and linking elements, generating and checking hypotheses and creating new concepts as they go along. Scientific Discovery examines the nature of scientific research and reviews the arguments for and against a normative theory of discovery; describes the evolution of the BACON programs, which discover quantitative empirical laws and invent new concepts; presents programs that discover laws in qualitative and quantitative data; and ties the results together, suggesting how a combined and extended program might find research problems, invent new instruments, and invent appropriate problem representations. Numerous prominent historical examples of discoveries from physics and chemistry are used as tests for the programs and anchor the discussion concretely in the history of science.
Machine Learning In Action
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Author : Peter Harrington
language : en
Publisher: Manning
Release Date : 2012-04-19
Machine Learning In Action written by Peter Harrington and has been published by Manning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-04-19 with Computers categories.
Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce