Graph Machine Learning Mastery
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Graph Machine Learning Mastery
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Author : Philip Oscar
language : en
Publisher: Independently Published
Release Date : 2025-12-17
Graph Machine Learning Mastery written by Philip Oscar and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-17 with Computers categories.
Graph Machine Learning Mastery A Complete Guide to Graph Neural Networks, Graph Transformers, Temporal GNNs, and LLM-Powered Graph AI with PyTorch Geometric & DGL Graph-structured data powers today's most advanced AI systems-from recommendation engines and fraud detection to drug discovery, cybersecurity, and large-scale knowledge graphs. Graph Machine Learning Mastery is the definitive, end-to-end guide for engineers, researchers, and data scientists who want to design, train, scale, and deploy production-ready graph AI systems using state-of-the-art techniques. This book goes far beyond theory. You'll master Graph Neural Networks (GNNs), Graph Transformers, Temporal & Dynamic Graph Models, and LLM-augmented Graph AI, all with hands-on implementations using industry-standard frameworks like and . What You'll Learn Build powerful GNN architectures: GCN, GAT, GraphSAGE, GIN, heterogeneous and large-scale GNNs Transition from GNNs to Graph Transformers with positional encodings and attention mechanisms Model temporal and dynamic graphs using TGN, TGAT, DySAT, and continuous-time message passing Design LLM + GNN hybrid systems for reasoning, knowledge graphs, and GraphRAG pipelines Apply graph ML to real-world domains: fraud detection, recommender systems, molecular graphs, finance, telecom, and cybersecurity Train, optimize, monitor, and deploy graph models in production environments Integrate GNNs with graph databases, MLOps pipelines, and scalable inference system. Hands-On, End-to-End Projects You'll implement complete production-grade projects including: Node classification, graph classification, and link prediction Temporal graph forecasting Molecular property prediction with OGB benchmarks Graph-augmented LLM systems for intelligent reasoning and recommendation. Each project walks you through data preprocessing, model architecture, training, evaluation, deployment, and monitoring-so you don't just learn concepts, you build real systems. Who This Book Is For Data scientists and ML engineers expanding into graph-based AI AI researchers exploring next-generation GNN and Transformer architectures Backend and platform engineers deploying graph intelligence at scale Professionals working with knowledge graphs, recommendation systems, and complex networks A working knowledge of Python and basic machine learning is recommended. Why This Book Stands Out Unlike fragmented tutorials or outdated references, Graph Machine Learning Mastery delivers a modern, unified, and production-focused roadmap-from classical graph learning to cutting-edge LLM-powered Graph AI. With deep technical insight, real-world case studies, and extensive appendices packed with APIs, cheat sheets, troubleshooting guides, and learning paths, this book is designed to become your long-term reference and career accelerator. If you're serious about mastering Graph Machine Learning, Graph Transformers, Temporal GNNs, and LLM-driven AI systems, this is the book you've been waiting for.
Kickstart Unsupervised Machine Learning Master Unsupervised Machine Learning Through Pattern Discovery Clustering And Dimensionality Reduction To Build Intelligent Real World Applications
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Author : Dr. Nimrita
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2025-12-27
Kickstart Unsupervised Machine Learning Master Unsupervised Machine Learning Through Pattern Discovery Clustering And Dimensionality Reduction To Build Intelligent Real World Applications written by Dr. Nimrita and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-27 with Computers categories.
Unlock the power of unsupervised learning to uncover hidden insights and transform raw data into actionable knowledge. Key Features● Master unsupervised learning techniques for Machine Learning with real-world applications.● Learn clustering, dimensionality reduction, and anomaly detection with real-world applications.● Build practical expertise through step-by-step coding and practical examples as well as datasets. Book DescriptionUnsupervised machine learning is revolutionizing how organizations extract value from raw data, revealing patterns and structures without predefined labels. From customer segmentation and fraud detection to generative modeling, its versatility drives innovation across industries. Kickstart Unsupervised Machine Learning is your comprehensive companion to mastering this transformative field. Starting with the core principles, the book introduces essential clustering algorithms—including K-Means, DBSCAN, and hierarchical approaches—before advancing to dimensionality reduction techniques such as PCA, t-SNE, and UMAP for simplifying complex data. It then explores sophisticated models like Gaussian Mixture Models and Generative Adversarial Networks (GANs), combining theory with practical coding exercises and hands-on projects using real-world datasets to solidify your understanding. Thus, by the end of this book, you will confidently evaluate, deploy, and optimize unsupervised models to derive meaningful insights from unstructured data. What you will learn● Understand the principles and algorithms of unsupervised learning from ground-up.● Apply clustering and dimensionality reduction techniques on complex datasets.● Evaluate and visualize models using key performance metrics such as validation and interpretability.● Implement unsupervised workflows using Python and open datasets.● Solve real-world challenges in NLP, image, and anomaly detection.● Extend learning methods to research and production-level projects.
Proceedings
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Author :
language : en
Publisher:
Release Date : 2004
Proceedings written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Software engineering categories.
Master S Theses Directories
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Author :
language : en
Publisher:
Release Date : 1993
Master S Theses Directories written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Dissertations, Academic categories.
"Education, arts and social sciences, natural and technical sciences in the United States and Canada".
Mastering Psychology
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Author : Lester A. Lefton
language : en
Publisher:
Release Date : 1986
Mastering Psychology written by Lester A. Lefton and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986 with Psychology categories.
Library Hi Tech Bibliography
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Author :
language : en
Publisher:
Release Date : 1995
Library Hi Tech Bibliography written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Libraries categories.
Mastering Tensorflow 1 X
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Author : Armando Fandango
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-01-22
Mastering Tensorflow 1 X written by Armando Fandango 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 2018-01-22 with Computers categories.
Build, scale, and deploy deep neural network models using the star libraries in Python Key Features Delve into advanced machine learning and deep learning use cases using Tensorflow and Keras Build, deploy, and scale end-to-end deep neural network models in a production environment Learn to deploy TensorFlow on mobile, and distributed TensorFlow on GPU, Clusters, and Kubernetes Book Description TensorFlow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. TensorFlow represents the data as tensors and the computation as graphs. This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Leverage the power of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Throughout the book, you will obtain hands-on experience with varied datasets, such as MNIST, CIFAR-10, PTB, text8, and COCO-Images. You will learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF Clusters, deploy production models with TensorFlow Serving, and build and deploy TensorFlow models for mobile and embedded devices on Android and iOS platforms. You will see how to call TensorFlow and Keras API within the R statistical software, and learn the required techniques for debugging when the TensorFlow API-based code does not work as expected. The book helps you obtain in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems. By the end of this guide, you will have mastered the offerings of TensorFlow and Keras, and gained the skills you need to build smarter, faster, and efficient machine learning and deep learning systems. What you will learn Master advanced concepts of deep learning such as transfer learning, reinforcement learning, generative models and more, using TensorFlow and Keras Perform supervised (classification and regression) and unsupervised (clustering) learning to solve machine learning tasks Build end-to-end deep learning (CNN, RNN, and Autoencoders) models with TensorFlow Scale and deploy production models with distributed and high-performance computing on GPU and clusters Build TensorFlow models to work with multilayer perceptrons using Keras, TFLearn, and R Learn the functionalities of smart apps by building and deploying TensorFlow models on iOS and Android devices Supercharge TensorFlow with distributed training and deployment on Kubernetes and TensorFlow Clusters Who this book is for This book is for data scientists, machine learning engineers, artificial intelligence engineers, and for all TensorFlow users who wish to upgrade their TensorFlow knowledge and work on various machine learning and deep learning problems. If you are looking for an easy-to-follow guide that underlines the intricacies and complex use cases of machine learning, you will find this book extremely useful. Some basic understanding of TensorFlow is required to get the most out of the book.
Machine Learning
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Author : D. Sleeman
language : en
Publisher: Morgan Kaufmann
Release Date : 1992
Machine Learning written by D. Sleeman and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Computers categories.
Machine Learning Proceedings 1992.
Mitigating Bias In Machine Learning
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Author : Carlotta A. Berry
language : en
Publisher: McGraw Hill Professional
Release Date : 2024-10-18
Mitigating Bias In Machine Learning written by Carlotta A. Berry 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 2024-10-18 with Technology & Engineering categories.
This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries. Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced. Mitigating Bias in Machine Learning addresses: Ethical and Societal Implications of Machine Learning Social Media and Health Information Dissemination Comparative Case Study of Fairness Toolkits Bias Mitigation in Hate Speech Detection Unintended Systematic Biases in Natural Language Processing Combating Bias in Large Language Models Recognizing Bias in Medical Machine Learning and AI Models Machine Learning Bias in Healthcare Achieving Systemic Equity in Socioecological Systems Community Engagement for Machine Learning
Becoming A Master Student
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Author : David B. Ellis
language : en
Publisher:
Release Date : 2003
Becoming A Master Student written by David B. Ellis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Education categories.
The concise version of this best-selling text accommodates shorter courses while still offering the key elements of the complete text by combining topics, and streamlining articles and activities.Explanation and terms in the Learning Style Inventory have been simplified for clarity and ease of use.A resources chapter focuses on financial strategies, community resources, and computer resources. In addition, a new article, Don't Let Debt Bring You Down, offers suggestions on preventing credit card debt and paying student loans.A significantly shorter format (10 chapters) makes the Concise suitable for 0, 1, and 2 credit courses.