A Greater Foundation For Machine Learning Engineering
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A Greater Foundation For Machine Learning Engineering
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Author : Dr Ganapathi Pulipaka
language : en
Publisher: Xlibris Us
Release Date : 2021-10
A Greater Foundation For Machine Learning Engineering written by Dr Ganapathi Pulipaka and has been published by Xlibris Us this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10 with categories.
The book provides foundations of machine learning and algorithms with a road map to deep learning, genesis of machine learning, installation of Python, supervised machine learning algorithms and implementations in Python or R, unsupervised machine learning algorithms in Python or R including natural language processing techniques and algorithms, Bayesian statistics, origins of deep learning, neural networks, and all the deep learning algorithms with some implementations in TensorFlow and architectures, installation of TensorFlow, neural net implementations in TensorFlow, Amazon ecosystem for machine learning, swarm intelligence, machine learning algorithms, in-memory computing, genetic algorithms, real-world research projects with supercomputers, deep learning frameworks with Intel deep learning platform, Nvidia deep learning frameworks, IBM PowerAI deep learning frameworks, H2O AI deep learning framework, HPC with deep learning frameworks, GPUs and CPUs, memory architectures, history of supercomputing, infrastructure for supercomputing, installation of Hadoop on Linux operating system, design considerations, e-Therapeutics's big data project, infrastructure for in-memory data fabric Hadoop, healthcare and best practices for data strategies, R, architectures, NoSQL databases, HPC with parallel computing, MPI for data science and HPC, and JupyterLab for HPC.
A Greater Foundation For Machine Learning Engineering
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Author : Dr. Ganapathi Pulipaka
language : en
Publisher: Xlibris Corporation
Release Date : 2021-10-01
A Greater Foundation For Machine Learning Engineering written by Dr. Ganapathi Pulipaka and has been published by Xlibris Corporation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-01 with Computers categories.
This research scholarly illustrated book has more than 250 illustrations. The simple models of supervised machine learning with Gaussian Naïve Bayes, Naïve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest neighbors, and support vector machines lay a more excellent foundation for statistics. The author of the book Dr. Ganapathi Pulipaka, a top influencer of machine learning in the US, has created this as a reference book for universities. This book contains an incredible foundation for machine learning and engineering beyond a compact manual. The author goes to extraordinary lengths to make academic machine learning and deep learning literature comprehensible to create a new body of knowledge. The book aims at readership from university students, enterprises, data science beginners, machine learning and deep learning engineers at scale for high-performance computing environments. A Greater Foundation of Machine Learning Engineering covers a broad range of classical linear algebra and calculus with program implementations in PyTorch, TensorFlow, R, and Python with in-depth coverage. The author does not hesitate to go into math equations for each algorithm at length that usually many foundational machine learning books lack leveraging the JupyterLab environment. Newcomers can leverage the book from University or people from all walks of data science or software lives to the advanced practitioners of machine learning and deep learning. Though the book title suggests machine learning, there are several implementations of deep learning algorithms, including deep reinforcement learning. The book's mission is to help build a strong foundation for machine learning and deep learning engineers with all the algorithms, processors to train and deploy into production for enterprise-wide machine learning implementations. This book also introduces all the concepts of natural language processing required for machine learning algorithms in Python. The book covers Bayesian statistics without assuming high-level mathematics or statistics experience from the readers. It delivers the core concepts and implementations required with R code with open datasets. The book also covers unsupervised machine learning algorithms with association rules and k-means clustering, metal-learning algorithms, bagging, boosting, random forests, and ensemble methods. The book delves into the origins of deep learning in a scholarly way covering neural networks, restricted Boltzmann machines, deep belief networks, autoencoders, deep Boltzmann machines, LSTM, and natural language processing techniques with deep learning algorithms and math equations. It leverages the NLTK library of Python with PyTorch, Python, and TensorFlow's installation steps, then demonstrates how to build neural networks with TensorFlow. Deploying machine learning algorithms require a blend of cloud computing platforms, SQL databases, and NoSQL databases. Any data scientist with a statistics background that looks to transition into a machine learning engineer role requires an in-depth understanding of machine learning project implementations on Amazon, Google, or Microsoft Azure cloud computing platforms. The book provides real-world client projects for understanding the complete implementation of machine learning algorithms. This book is a marvel that does not leave any application of machine learning and deep learning algorithms. It sets a more excellent foundation for newcomers and expands the horizons for experienced deep learning practitioners. It is almost inevitable that there will be a series of more advanced algorithms follow-up books from the author in some shape or form after setting such a perfect foundation for machine learning engineering.
Deep Learning With Python
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2016-05-13
Deep Learning With Python written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-13 with Computers categories.
Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this Ebook, learn exactly how to get started and apply deep learning to your own machine learning projects.
Basics Of Linear Algebra For Machine Learning
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2018-01-24
Basics Of Linear Algebra For Machine Learning written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-24 with Computers categories.
Linear algebra is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it. In this laser-focused Ebook, you will finally cut through the equations, Greek letters, and confusion, and discover the topics in linear algebra that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover what linear algebra is, the importance of linear algebra to machine learning, vector, and matrix operations, matrix factorization, principal component analysis, and much more.
Mathematics For Machine Learning
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Author : Marc Peter Deisenroth
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-23
Mathematics For Machine Learning written by Marc Peter Deisenroth and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-23 with Computers categories.
Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.
Foundations Of Machine Learning Second Edition
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Author : Mehryar Mohri
language : en
Publisher: MIT Press
Release Date : 2018-12-25
Foundations Of Machine Learning Second Edition written by Mehryar Mohri and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-25 with Computers categories.
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
Fenomena Artificial Intelligence Ai
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Author : Rony Sandra Yofa Zebua
language : id
Publisher: PT. Sonpedia Publishing Indonesia
Release Date : 2023-06-03
Fenomena Artificial Intelligence Ai written by Rony Sandra Yofa Zebua and has been published by PT. Sonpedia Publishing Indonesia this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-03 with Computers categories.
Buku "Fenomena Artificial Intelligence (AI)" adalah sebuah buku yang memperkenalkan konsep dan dampak dari kecerdasan buatan (AI) dalam kehidupan modern kita. Buku ini ditulis untuk memberikan pemahaman yang komprehensif tentang AI kepada pembaca, baik yang sudah akrab dengan teknologi maupun yang baru mengenalnya. Buku ini memulai dengan membahas pengenalan dasar mengenai kecerdasan buatan, menjelaskan pemanfaatan dasar di balik AI. Tim penulis mengungkapkan sistem pakar pada AI, dan Machine Learning sebagai pendekatan AI. Pembaca akan memahami bagaimana komputer dan mesin dapat mempelajari dan mengambil keputusan secara mandiri, serta kemampuan mereka untuk memproses data dengan kecepatan yang jauh melebihi kemampuan manusia. Selanjutnya, buku ini mengeksplorasi berbagai aplikasi AI yang telah membawa perubahan signifikan dalam berbagai industri, seperti Aplikasi AI untuk Mendukung Pendidikan, Kesehatan, Teknologi, Perpustakaan, Penjualan & Pemasaran, Transportasi dan Aplikasi AI untuk bidang wisata dan perjalanan. Pembaca akan melihat bagaimana AI mengubah cara kita bekerja, berinteraksi, dan menjalani kehidupan sehari-hari. Buku ini cocok untuk pembaca yang tertarik untuk memahami dampak revolusi AI serta ingin menggali implikasi dan tanggung jawab sosial yang melekat pada perkembangan teknologi ini.
Model Uncertainties In Foundation Design
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Author : Chong Tang
language : en
Publisher: CRC Press
Release Date : 2021-03-17
Model Uncertainties In Foundation Design written by Chong Tang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-17 with Technology & Engineering categories.
Model Uncertainties in Foundation Design is unique in the compilation of the largest and the most diverse load test databases to date, covering many foundation types (shallow foundations, spudcans, driven piles, drilled shafts, rock sockets and helical piles) and a wide range of ground conditions (soil to soft rock). All databases with names prefixed by NUS are available upon request. This book presents a comprehensive evaluation of the model factor mean (bias) and coefficient of variation (COV) for ultimate and serviceability limit state based on these databases. These statistics can be used directly for AASHTO LRFD calibration. Besides load test databases, performance databases for other geo-structures and their model factor statistics are provided. Based on this extensive literature survey, a practical three-tier scheme for classifying the model uncertainty of geo-structures according to the model factor mean and COV is proposed. This empirically grounded scheme can underpin the calibration of resistance factors as a function of the degree of understanding – a concept already adopted in the Canadian Highway Bridge Design Code and being considered for the new draft for Eurocode 7 Part 1 (EN 1997-1:202x). The helical pile research in Chapter 7 was recognised by the 2020 ASCE Norman Medal.
Advancements In Artificial Intelligence And Machine Learning
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Author : Asif Khan, Mohammad Kamrul Hasan, Naushad Varish, Mohammed Aslam Husain
language : en
Publisher: Bentham Science Publishers
Release Date : 2025-06-19
Advancements In Artificial Intelligence And Machine Learning written by Asif Khan, Mohammad Kamrul Hasan, Naushad Varish, Mohammed Aslam Husain and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-19 with Computers categories.
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing industries, reshaping the way we interact with technology, and driving innovation across multiple disciplines. Advancements in Artificial Intelligence and Machine Learning is a comprehensive exploration of the latest developments, applications, and challenges in AI and ML, offering insights into cutting-edge research and real-world implementations. This book is a collection of twelve chapters, each exploring a distinct application of Artificial Intelligence (AI) and Machine Learning (ML). It begins with an overview of AI’s transformative role in Next-Gen Mechatronics, followed by a comprehensive review of key advancements and trends in the field. The book then examines AI’s impact across diverse sectors, including energy, digital communication, and security, with topics such as AI-based aging analysis of power transformer oil, AI in social media management, and AI-driven human detection systems. Further chapters address sentiment analysis, visual analysis for image processing, and the integration of AI in smart grid networks. The volume also covers AI applications in hardware security for wireless sensor networks, drone robotics, and crime prevention systems. The final set of chapters highlight AI’s role in healthcare and automation, including an AI-assisted system for women’s safety in India and the use of EfficientNet B0 CNN architecture for brain tumor detection and classification. Together, these chapters showcase the versatility and growing influence of AI and ML across critical modern industries. Key features A multidisciplinary approach covering AI applications in robotics, cybersecurity, healthcare, and digital transformation in 12 organized chapters. A focus on contemporary challenges and solutions in AI and ML across industries. Research-driven insights from experts and practitioners in the field. Practical discussions on AI-driven automation, security, and intelligent decision-making systems.
Aws Machine Learning Engineer Associate Complete Study Guide
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Author : Abrielle Wang Perkins
language : en
Publisher:
Release Date :
Aws Machine Learning Engineer Associate Complete Study Guide written by Abrielle Wang Perkins and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Master the AWS Machine Learning Engineer Associate certification with the most comprehensive study guide available. This practical implementation guide combines 450+ exam-style practice questions with real-world MLOps scenarios to prepare you for both certification success and professional excellence. What You'll Learn: Complete coverage of all four exam domains: Data Engineering (28%), Exploratory Data Analysis (24%), Model Development (26%), and MLOps Implementation (22%) 450+ practice questions with detailed explanations matching actual exam complexity Real-world case studies from healthcare, finance, retail, and manufacturing industries Hands-on projects including multi-source data pipelines, automated feature engineering, and production deployment architectures SageMaker services mastery including Feature Store, Model Registry, Pipelines, and Monitoring MLOps best practices for CI/CD, model monitoring, governance, and compliance Cost optimization strategies for training, inference, and data storage Production deployment patterns for real-time endpoints, batch processing, and edge computing Key Features: Comprehensive Practice Exam: 250 additional questions covering all certification domains Implementation Focus: Real-world MLOps scenarios beyond theoretical concepts Service Integration: Deep coverage of 15+ AWS ML and data services Project-Based Learning: 22 hands-on projects with complete implementation guidance Industry Applications: Computer vision, NLP, time series, and traditional ML use cases Career Guidance: Interview preparation, portfolio development, and professional networking Perfect For: ML Engineers preparing for AWS MLA-C01 certification Data Scientists transitioning to MLOps roles Software Engineers entering machine learning operations IT professionals seeking AWS ML specialization Anyone building production ML systems on AWS Why This Book: The AWS Machine Learning Engineer Associate certification launched in 2024 represents the fastest-growing cloud certification. This comprehensive guide provides the only dedicated resource combining certification preparation with practical MLOps implementation skills. Stop struggling with scattered resources and theoretical content. Get the definitive guide that bridges certification requirements with real-world professional skills. Start your journey to AWS ML certification success and career advancement today.