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• Artocarpus Classification Technique using Deep Learning based Convolutional Neural Network.- Rambutan Image Classification using Various Deep Learning Approaches.- Mango Varieties Classification-based Optimization with Transfer Learning and Deep Learning Approaches.- Salak Image Classification Method based Deep Learning Technique using Two Transfer Learning Models.- Image Processing Identification for Sapodilla Using Convolution Neural Network (CNN) and Transfer Learning Techniques.- Comparison of Pre-trained and Convolutional Neural Networks for Classification of Jackfruit Artocarpus Integer and Artocarpus Heterophyllus.- Markisa/Passion Fruit Image Classification based Improved Deep Learning Approach using Transfer Learning.- Enhanced MapReduce Performance for the Distributed Parallel Computing: Application of the Big Data.- A Novel Big Data Classification Technique for Healthcare Application using Support Vector Machine, Random Forest and J48.- Comparative Study on Arabic Text Classification: Challenges and Opportunities.- Pedestrian Speed Prediction Using Feed Forward Neural Network.- Arabic Text Classification using Modified Artificial Bee Colony Algorithm for Sentiment Analysis: The Case of Jordanian Dialect.
• (source: Nielsen Book Data)

This book is very beneficial for early researchers/faculty who want to work in deep learning and machine learning for the classification domain. It helps them study, formulate, and design their research goal by aligning the latest technologies studies' image and data classifications. The early start-up can use it to work with product or prototype design requirement analysis and its design and development.
(source: Nielsen Book Data)

Most intermediate-level machine learning books usually focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance and the need to be able to explain why and how your ML model makes the predictions that it does. This practical guide brings together the best-in-class techniques for model interpretability and explains model predictions in a hands-on approach. Experienced ML practitioners will be able to more easily apply these tools in their daily workflow.
(source: Nielsen Book Data)

• Introduction 1.1 Artificial neural networks and deep learning
• 1.2 Evolutionary optimization and learning
• 1.3 Privacy-preserving computation
• 1.4 Federated learning
• 1.5 Summary
• Communication-Efficient
• Federated Learning 2.1 Communication cost in federated learning
• 2.2 Main methodologies
• 2.3 Temporally weighted averaging and layer-wise weight update
• 2.4 Trained ternary compression for federated learning
• 2.5 Summary
• Evolutionary
• Federated Learning
• 3.1 Motivations and challenges
• 3.2 Offline evolutionary multi-objective federated learning
• 3.3 Realtime evolutionary federated neural architecture search
• 3.4 Summary
• Secure
• Federated Learning
• 4.1 Threats to federated learning
• 4.2 Distributed encryption for horizontal federated learning
• 4.3 Secure vertical federated learning
• 4.4 Summary
• Summary
• and Outlook
• 5.1 Summary
• 5.2 Future directions.
• (source: Nielsen Book Data)

This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements. The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionary learning, and privacy preservation. The book is particularly well suited for graduate students, academic researchers, and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background, or as a reference text for graduate courses. .
(source: Nielsen Book Data)

• Editorial Note.- Artificial Intelligence as Dual-Use Technology.- Diabetic Retinopathy Detection using Transfer and Reinforcement Learning with effective image preprocessing and data augmentation techniques. .
• (source: Nielsen Book Data)

This book aims at updating the relevant computer science-related research communities, including professors, researchers, scientists, engineers and students, as well as the general reader from other disciplines, on the most recent advances in applications of methods based on Fusing Machine Learning Paradigms. Integrated or Hybrid Machine Learning methodologies combine together two or more Machine Learning approaches achieving higher performance and better efficiency when compared to those of their constituent components and promising major impact in science, technology and the society. The book consists of an editorial note and an additional eight chapters and is organized into two parts, namely: (i) Recent Application Areas of Fusion of Machine Learning Paradigms and (ii) Applications that can clearly benefit from Fusion of Machine Learning Paradigms. This book is directed toward professors, researchers, scientists, engineers and students in Machine Learning-related disciplines, as the hybridism presented, and the case studies described provide researchers with successful approaches and initiatives to efficiently address complex classification or regression problems. It is also directed toward readers who come from other disciplines, including Engineering, Medicine or Education Sciences, and are interested in becoming versed in some of the most recent Machine Learning-based technologies. Extensive lists of bibliographic references at the end of each chapter guide the readers to probe further into the application areas of interest to them.
(source: Nielsen Book Data)

With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world. You'll learn: Methods to explain ML models and their outputs to stakeholders How to recognize and fix fairness concerns and privacy leaks in an ML pipeline How to develop ML systems that are robust and secure against malicious attacks Important systemic considerations, like how to manage trust debt and which ML obstacles require human intervention.
(source: Nielsen Book Data)

• 1. Introduction.-
• 2. Representation.-
• 3. Nearest Neighbor Algorithms.-
• 4. Representation Using Linear Combinations.-
• 5. Non-Linear Schemes for Representation.-
• 6. Conclusions.
• (source: Nielsen Book Data)

This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and practice, when they are confronted with the huge size of the underlying data. Solutions to this problem are aptly covered in the book. In addition, the book covers a wide range of representation techniques that are important for academics and ML practitioners alike, such as Locality Sensitive Hashing (LSH), Distance Metrics and Fractional Norms, Principal Components (PCs), Random Projections and Autoencoders. Several experimental results are provided in the book to demonstrate the discussed techniques' effectiveness.
(source: Nielsen Book Data)

• Intro
• Preface
• Contents
• 1: Data Science Process
• Traditional Model Building
• Modern Approach for Model Building
• AI on Image Datasets
• Model Development on Text Datasets
• Model Building on High-Frequency Datasets
• Data Science Process
• Data Preparation
• Numeric Data Processing
• Text Processing
• Preprocessing Text Data
• Exploratory Data Analysis
• Features Engineering
• Deciding on Model Type
• Model Training
• Algorithm Selection
• AutoML
• Hyper-Parameter Tuning
• Model Building Using ANN
• Models Based on Transfer Learning
• Summary

• 2: Dimensionality Reduction
• In a Nutshell
• Why Reduce Dimensionality?
• Dimensionality Reduction Techniques
• Project Dataset
• Columns with Missing Values
• Filtering Columns Based on Variance
• Filtering Highly Correlated Columns
• Random Forest
• Backward Elimination
• Forward Features Selection
• Factor Analysis
• Principal Component Analysis
• PCA on Huge Multi-columnar Dataset
• About the Dataset
• Loading Dataset
• Model Building
• PCA for Visualization
• PCA for Model Building
• Independent Component Analysis
• Isometric Mapping

• T-Distributed Stochastic Neighbor Embedding (t-SNE)
• UMAP
• Singular Value Decomposition
• Linear Discriminant Analysis (LDA)
• Summary
• Part I: Classical Algorithms: Overview
• 3: Regression Analysis
• In a Nutshell
• When to Use?
• Regression Types
• Linear Regression
• Assumptions
• Polynomial Regression
• Ridge Regression
• Lasso Regression
• ElasticNet Regression
• Linear Regression Implementations
• Linear Regression
• Ridge Regression
• Lasso Regression
• Bayesian Linear Regression
• BLR Implementation
• BLR Project
• Logistic Regression

• Logistic Regression Implementation
• Guidelines for Model Selection
• Whatś Next?
• Summary
• 4: Decision Tree
• In a Nutshell
• Wide Range of Applications
• Decision Tree Workings
• Tree Traversal
• Tree Construction
• Entropy
• Information Gain
• Gini Index
• Constructing Tree
• Tree Construction Algorithm
• Tree Traversal Algorithm
• Implementation
• Project (Regression)
• Loading Dataset
• Preparing Datasets
• Model Building
• Evaluating Performance
• Tree Visualization
• Feature Importance
• Project (Classifier)
• Summary
• 5: Ensemble: Bagging and Boosting

• What is Bagging and Boosting?
• Bagging
• Boosting
• Random Forest
• In a Nutshell
• What Is Random Forest?
• Random Forest Algorithm
• Advantages
• Applications
• Implementation
• Random Forest Project
• ExtraTrees
• Bagging Ensemble Project
• ExtraTreesRegressor
• ExtraTreesClassifier
• Bagging
• BaggingRegressor
• BaggingClassifier
• AdaBoost
• How Does It Work?
• Implementation
• AdaBoostRegressor
• AdaBoost Classifier
• Advantages/Disadvantages
• Gradient Boosting
• Loss Function
• Requirements for Gradient Boosting
• Implementation
• GradientBoostingRegressor

This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single Cheat Sheet. The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big.

• Part 1: Traditional Machine Learning Approaches 1. User Vs. Machine Seismic Attribute Selection for Unsupervised Machine Learning Techniques: Does Human Insight Provide Better Results Than Statistically Chosen Attributes? 2. Relative Performance of Support Vector Machine, Decision Trees, and Random Forest Classifiers for Predicting Production Success in US unconventional Shale Plays
• Part 2: Deep Learning Approaches 3. Recurrent Neural Network: application in facies classification 4. Recurrent Neural Network for Seismic Reservoir Characterization 5. Application of Convolutional Neural Networks for the Classification of Siliciclastic Core Photographs 6. Convolutional Neural Networks for Fault Interpretation - Case Study Examples around the World
• Part 3: Physics-based Machine Learning Approaches 7. Scientific Machine Learning for Improved Seismic Simulation and Inversion 8. Prediction of Acoustic Velocities using Machine Learning 9. Regularized Elastic Full Waveform Inversion using Deep Learning 10. A Holistic Approach to Computing First-arrival Traveltimes using Neural Networks
• Part 4: New Directions 11. Application of Artificial Intelligence to Computational Fluid Dynamics.
• (source: Nielsen Book Data)

Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches brings together the fundamentals of popular and emerging machine learning (ML) algorithms with their applications in subsurface analysis, including geology, geophysics, petrophysics, and reservoir engineering. The book is divided into four parts: traditional ML, deep learning, physics-based ML, and new directions, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorithm with a detailed workflow for a specific application in geosciences. Some chapters also compare the results from an algorithm with others to better equip the readers with different strategies to implement automated workflows for subsurface analysis. Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches will help researchers in academia and professional geoscientists working on the subsurface-related problems (oil and gas, geothermal, carbon sequestration, and seismology) at different scales to understand and appreciate current trends in ML approaches, their applications, advances and limitations, and future potential in geosciences by bringing together several contributions in a single volume.
(source: Nielsen Book Data)

• Table of Contents Foundational Concepts of Explainability Techniques Model Explainability Methods Data-Centric Approaches LIME for Model Interpretability Practical Exposure to Using LIME in ML Model Interpretability Using SHAP Practical Exposure to Using SHAP in ML Human-Friendly Explanations with TCAV Other Popular XAI Frameworks XAI Industry Best Practices End User-Centered Artificial Intelligence.
• (source: Nielsen Book Data)

Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems Key Features Explore various explainability methods for designing robust and scalable explainable ML systems Use XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problems Design user-centric explainable ML systems using guidelines provided for industrial applications Book DescriptionExplainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases. Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin by gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users. By the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered. What you will learn Explore various explanation methods and their evaluation criteria Learn model explanation methods for structured and unstructured data Apply data-centric XAI for practical problem-solving Hands-on exposure to LIME, SHAP, TCAV, DALEX, ALIBI, DiCE, and others Discover industrial best practices for explainable ML systems Use user-centric XAI to bring AI closer to non-technical end users Address open challenges in XAI using the recommended guidelines Who this book is forThis book is for scientists, researchers, engineers, architects, and managers who are actively engaged in machine learning and related fields. Anyone who is interested in problem-solving using AI will benefit from this book. Foundational knowledge of Python, ML, DL, and data science is recommended. AI/ML experts working with data science, ML, DL, and AI will be able to put their knowledge to work with this practical guide. This book is ideal for you if you're a data and AI scientist, AI/ML engineer, AI/ML product manager, AI product owner, AI/ML researcher, and UX and HCI researcher.
(source: Nielsen Book Data)

• Table of Contents An Overview of the Machine Learning Life Cycle What Problems Do Feature Stores Solve? Feature Store Fundamentals, Terminology, and Usage Adding Feature Store to ML Models Model Training and Inference Model to Production and Beyond Feast Alternatives and ML Best Practices Use Case - Customer Churn Prediction.
• (source: Nielsen Book Data)

Learn how to leverage feature stores to make the most of your machine learning models Key Features Understand the significance of feature stores in the ML life cycle Discover how features can be shared, discovered, and re-used Learn to make features available for online models during inference Book DescriptionFeature store is one of the storage layers in machine learning (ML) operations, where data scientists and ML engineers can store transformed and curated features for ML models. This makes them available for model training, inference (batch and online), and reuse in other ML pipelines. Knowing how to utilize feature stores to their fullest potential can save you a lot of time and effort, and this book will teach you everything you need to know to get started. Feature Store for Machine Learning is for data scientists who want to learn how to use feature stores to share and reuse each other's work and expertise. You'll be able to implement practices that help in eliminating reprocessing of data, providing model-reproducible capabilities, and reducing duplication of work, thus improving the time to production of the ML model. While this ML book offers some theoretical groundwork for developers who are just getting to grips with feature stores, there's plenty of practical know-how for those ready to put their knowledge to work. With a hands-on approach to implementation and associated methodologies, you'll get up and running in no time. By the end of this book, you'll have understood why feature stores are essential and how to use them in your ML projects, both on your local system and on the cloud. What you will learn Understand the significance of feature stores in a machine learning pipeline Become well-versed with how to curate, store, share and discover features using feature stores Explore the different components and capabilities of a feature store Discover how to use feature stores with batch and online models Accelerate your model life cycle and reduce costs Deploy your first feature store for production use cases Who this book is forIf you have a solid grasp on machine learning basics, but need a comprehensive overview of feature stores to start using them, then this book is for you. Data/machine learning engineers and data scientists who build machine learning models for production systems in any domain, those supporting data engineers in productionizing ML models, and platform engineers who build data science (ML) platforms for the organization will also find plenty of practical advice in the later chapters of this book.
(source: Nielsen Book Data)

• Introduction to Federated Learning.- Tree-Based Models for Federated Learning Systems.- Semantic Vectorization: Text and Graph-Based Models.- Personalization in Federated Learning.- Personalized, Robust Federated Learning with Fed+.- Communication-Efficient Distributed Optimization Algorithms.- Communication-Efficient Model Fusion.- Federated Learning and Fairness.- Introduction to Federated Learning Systems.- Local Training and Scalability of Federated Learning Systems.- Straggler Management.- Systems Bias in Federated Learning.- Protecting Against Data Leakage in Federated Learning: What Approach Should You Choose?.- Private Parameter Aggregation for Federated Learning.- Data Leakage in Federated Learning.- Security and Robustness in Federated Machine Learning.- Dealing with Byzantine Threats to Neural Networks.- Privacy-Preserving Vertical Federated Learning.- Split Learning: A Resource Efficient Model & Data Parallel Approach for Distributed Deep Learning.- Federated Learning for Collaborative Financial Crimes Detection.- Federated Reinforcement Learning for Portfolio Management.- Application of Federated Learning in Medical Imaging.- Advancing Healthcare Solutions with Federated Learning.- A Privacy-preserving Product Recommender System.- Application of Federated Learning in Telecommunications and Edge Computing.
• (source: Nielsen Book Data)

Federated Learning: A Comprehensive Overview of Methods and Applications presents an in-depth discussion of the most important issues and approaches to federated learning for researchers and practitioners. Federated Learning (FL) is an approach to machine learning in which the training data are not managed centrally. Data are retained by data parties that participate in the FL process and are not shared with any other entity. This makes FL an increasingly popular solution for machine learning tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory or practical reasons. This book explains recent progress in research and the state-of-the-art development of Federated Learning (FL), from the initial conception of the field to first applications and commercial use. To obtain this broad and deep overview, leading researchers address the different perspectives of federated learning: the core machine learning perspective, privacy and security, distributed systems, and specific application domains. Readers learn about the challenges faced in each of these areas, how they are interconnected, and how they are solved by state-of-the-art methods. Following an overview on federated learning basics in the introduction, over the following 24 chapters, the reader will dive deeply into various topics. A first part addresses algorithmic questions of solving different machine learning tasks in a federated way, how to train efficiently, at scale, and fairly. Another part focuses on providing clarity on how to select privacy and security solutions in a way that can be tailored to specific use cases, while yet another considers the pragmatics of the systems where the federated learning process will run. The book also covers other important use cases for federated learning such as split learning and vertical federated learning. Finally, the book includes some chapters focusing on applying FL in real-world enterprise settings.
(source: Nielsen Book Data)

• Cover
• Title
• Copyright
• End User License Agreement
• Contents
• Foreword
• Preface
• [Key Features]
• Key Features
• List of Contributors
• Cutting Edge Techniques of Adaptive Machine Learning for Image Processing and Computer Vision
• P. Sasikumar1 and T. Saravanan1, *
• INTRODUCTION
• Techniques for Improvising Images
• Spatial-Domain Method
• Frequency-Domain Method
• TRANSFORMS: IMAGE IMPROVEMENT
• Wavelet-Transform Oriented Image Improvement
• Scaling and Translation
• IMAGE IMPROVEMENT WITH FILTERS
• DENOISING OF IMAGES
• Frontward Transform

• IMAGE IMPROVEMENT WITH PRINCIPAL COMPONENT PCA FOR 2D
• Implementing 2D-PCA
• SELECTION AND EXTRACTION OF FEATURES
• Criteria for Selecting Features
• Linear Criteria for Extracting Features
• Discontinuity Handling
• Integration Part: Limitations
• Alteration of Smoothness Terminology
• CONCLUSION
• CONSENT FOR PUBLICATION
• CONFLICT OF INTEREST
• ACKNOWLEDGEMENT
• REFERENCES
• Algorithm For Intelligent Systems
• Pratik Dhoke1, *, Pranay Saraf1, Pawan Bhalandhare1, Yogadhar Pandey1, H. R. Deshmukh1 and Rahul Agrawal1
• INTRODUCTION
• Reinforcement Learning
• Q-Learning

• Game Theory
• Machine Learning
• Decision Tree
• Logistic Regression
• K-Means Clustering
• Artificial Neural Network (ANN)
• Swarm Intelligence
• Swarm Robots
• Swarm Intelligence in Decision Making Algorithm
• Natural Language Processing
• CONCLUSION
• FUTURE SCOPE
• CONSENT FOR PUBLICATION
• CONFLICT OF INTEREST
• ACKNOWLEDGEMENTS
• REFERENCES
• Clinical Decision Support System for Early Prediction of Congenital Heart Disease using Machine learning Techniques
• Ritu Aggarwal1, * and Suneet Kumar2
• INTRODUCTION
• RELATED WORK
• PROPOSED METHODOLOGY AND DATASET

• STEPS FOR TRAINING AND TESTING THE DATASET
• MACHINE LEARNING ALGORITHMS FOR PREDICTION
• SUPPORT VECTOR MACHINE
• RANDOM FOREST
• MULTILAYER PERCEPTRON
• INPUT LAYER
• HIDDEN LAYER
• OUTPUT LAYER
• K- NEAREST NEIGHBOR (K-NN)
• EXPERIMENTS AND RESULTS
• Comparison Results
• CONCLUSION
• CONSENT FOR PUBLICATION
• CONFLICT OF INTEREST
• ACKNOWLEDGEMENTS
• REFERENCES
• A Review on Covid-19 Pandemic and Role of Multilingual Information Retrieval and Machine Translation for Managing its Effect
• Mangala Madankar1, * and Manoj Chandak2
• INTRODUCTION
• RELATED WORK

• OUTBREAK STAGE OF COVID 19
• Travel history from infected countries
• Local Transmission
• Geographical Cluster of Cases
• Community Transmission
• CURRENT SITUATION IN INDIA
• TREATMENT
• ILLNESS SEVERITY
• ANTIBODY AND PLASMA THERAPY
• VACCINE
• PREVENTIVE MEASURE
• Myths
• EMERGING TECHNOLOGY FOR MITIGATING THE EFFECT OF THE COVID-19 PANDEMIC
• Infodemic and Natural Language Processing
• Arogya Setu App
• Issues of Languages all Over the World and Machine Translation
• Difficulties in Accessing Data in the Native Language
• INFORMATION RETRIEVAL SYSTEM FOR COVID-19

This book is a quick review of machine learning methods for engineeringapplications. It provides an introduction to the principles of machine learningand common algorithms in the first section. Proceeding chapters summarize andanalyze the existing scholarly work and discuss some general issues in this field.Next, it offers some guidelines on applying machine learning methods to softwareengineering tasks. Finally, it gives an outlook into some of the futuredeve.

"An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impactsPatterns, Predictions, and actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. The text: provides a modern introduction to machine learning, showing how patterns in data support predictions and consequential actions, pays special attention to societal impacts and fairness in decision making, and traces the development of machine learning from its origins to today. Also features a novel chapter on machine learning benchmarks and datasets and invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra. An essential textbook for students and a guide for researchers"-- Provided by publisher

• Table of Contents Deep Learning Life Cycle and MLOps Challenges Getting Started with MLflow for Deep Learning Tracking Models, Parameters, and Metrics Tracking Code and Data Versioning Running DL Pipelines in Different Environments Running Hyperparameter Tuning at Scale Multi-Step Deep Learning Inference Pipeline Deploying a DL Inference Pipeline at Scale Fundamentals of Deep Learning Explainability Implementing DL Explainability with MLflow.
• (source: Nielsen Book Data)

Train, test, run, track, store, tune, deploy, and explain provenance-aware deep learning models and pipelines at scale with reproducibility using MLflow Key Features Focus on deep learning models and MLflow to develop practical business AI solutions at scale Ship deep learning pipelines from experimentation to production with provenance tracking Learn to train, run, tune and deploy deep learning pipelines with explainability and reproducibility Book DescriptionThe book starts with an overview of the deep learning (DL) life cycle and the emerging Machine Learning Ops (MLOps) field, providing a clear picture of the four pillars of deep learning: data, model, code, and explainability and the role of MLflow in these areas. From there onward, it guides you step by step in understanding the concept of MLflow experiments and usage patterns, using MLflow as a unified framework to track DL data, code and pipelines, models, parameters, and metrics at scale. You'll also tackle running DL pipelines in a distributed execution environment with reproducibility and provenance tracking, and tuning DL models through hyperparameter optimization (HPO) with Ray Tune, Optuna, and HyperBand. As you progress, you'll learn how to build a multi-step DL inference pipeline with preprocessing and postprocessing steps, deploy a DL inference pipeline for production using Ray Serve and AWS SageMaker, and finally create a DL explanation as a service (EaaS) using the popular Shapley Additive Explanations (SHAP) toolbox. By the end of this book, you'll have built the foundation and gained the hands-on experience you need to develop a DL pipeline solution from initial offline experimentation to final deployment and production, all within a reproducible and open source framework. What you will learn Understand MLOps and deep learning life cycle development Track deep learning models, code, data, parameters, and metrics Build, deploy, and run deep learning model pipelines anywhere Run hyperparameter optimization at scale to tune deep learning models Build production-grade multi-step deep learning inference pipelines Implement scalable deep learning explainability as a service Deploy deep learning batch and streaming inference services Ship practical NLP solutions from experimentation to production Who this book is forThis book is for machine learning practitioners including data scientists, data engineers, ML engineers, and scientists who want to build scalable full life cycle deep learning pipelines with reproducibility and provenance tracking using MLflow. A basic understanding of data science and machine learning is necessary to grasp the concepts presented in this book.
(source: Nielsen Book Data)

• Introduction.- Introduction to ANN.- Introduction to Deep Learning.- Deep Soft Computing using Python.- Working with Keras.- Deep learning Applications using Python.- Advanced Deep learning techniques.- Conclusion.
• (source: Nielsen Book Data)

This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time applications. This book introduces a broad range of topics in deep learning. The authors start with the fundamentals, architectures, tools needed for effective implementation for scientists. They then present technical exposure towards deep learning using Keras, Tensorflow, Pytorch and Python. They proceed with advanced concepts with hands-on sessions for deep learning. Engineers, scientists, researches looking for a practical approach to deep learning will enjoy this book. Presents practical basics to advanced concepts in deep learning and how to apply them through various projects; Discusses topics such as deep learning in smart grids and renewable energy & sustainable development; Explains how to implement advanced techniques in deep learning using Pytorch, Keras, Python programming.
(source: Nielsen Book Data)

• Part I Preliminaries1 Introduction to Context-Aware Machine Learning and Mobile Data
• Analytics
• 1.1 Introduction
• 1.2 Context-Aware Machine Learning
• 1.3 Mobile Data Analytics
• 1.4 An Overview of this Book
• 1.5 Conclusion
• References
• 2 Application Scenarios and Basic Structure for Context-Aware
• Machine Learning Framework
• 2.1 Motivational Examples with Application Scenarios
• 2.2 Structure and Elements of Context-Aware Machine Learning
• Framework
• 2.2.1 Contextual Data Acquisition
• 2.2.2 Context Discretization
• 2.2.3 Contextual Rule Discovery
• 2.2.4 Dynamic Updating and Management of Rules
• 2.3 Conclusion
• References
• 3 A Literature Review on Context-Aware Machine Learning and
• Mobile Data Analytics
• 3.1 Contextual Information
• 3.1.1 Definitions of Contexts
• 3.1.2 Understanding the Relevancy of Contexts
• 3.2 Context Discretization
• 3.2.1 Discretization of Time-Series Data
• 3.2.2 Static Segmentation
• vii
• viii Contents
• 3.2.3 Dynamic Segmentation
• 3.3 Rule Discovery
• 3.3.1 Association Rule Mining
• 3.3.2 Classification Rules
• 3.4 Incremental Learning and Updating
• 3.5 Identifying the Scope of Research
• 3.6 Conclusion
• References
• Part II Context-Aware Rule Learning and Management
• 4 Contextual Mobile Datasets, Pre-processing and Feature Selection
• 4.1 Smart Mobile Phone Data and Associated Contexts
• 4.1.1 Phone Call Log
• 4.1.2 Mobile SMS Log
• 4.1.3 Smartphone App Usage Log
• 4.1.4 Mobile Phone Notification Log
• 4.1.5 Web or Navigation Log
• 4.1.6 Game Log
• 4.1.7 Smartphone Life Log
• 4.1.8 Dataset Summary
• 4.2 Examples of Contextual Mobile Phone Data
• 4.2.1 Time-Series Mobile Phone Data
• 4.2.2 Mobile phone data with multi-dimensional contexts
• 4.2.3 Contextual Apps Usage Data
• 4.3 Data Preprocessing
• 4.3.1 Data Cleaning
• 4.3.2 Data Integration
• 4.3.3 Data Transformation
• 4.3.4 Data Reduction
• 4.4 Dimensionality Reduction
• 4.4.1 Feature Selection
• 4.4.2 Feature Extraction
• 4.4.3 Dimensionality Reduction Algorithms
• 4.5 Conclusion
• References
• 5 Discretization of Time-Series Behavioral Data and Rule Generation
• based on Temporal Context
• 5.1 Introduction
• 5.2 Requirements Analysis
• 5.3 Time-series Segmentation Approach
• 5.3.1 Approach Overview
• 5.3.2 Initial Time Slices Generation
• 5.3.3 Behavior-Oriented Segments Generation
• Contents ix
• 5.3.4 Selection of Optimal Segmentation
• 5.3.5 Temporal Behavior Rule Generation using Time Segments
• 5.4 Effectiveness Comparison
• 5.5 Conclusion
• References
• 6 Discovering User Behavioral Rules based on Multi-dimensional
• Contexts
• 6.1 Introduction
• 6.2 Multi-dimensional Contexts in User Behavioral Rules
• 6.3 Requirements Analysis
• 6.4 Rule Mining Methodology
• 6.4.1 Identifying the Precedence of Context
• 6.4.2 Designing Association Generation Tree
• 6.4.3 Extracting Non-Redundant Behavioral Association Rules
• 6.5 Experimental Analysis
• 6.5.1 Effect on the Number of Produced Rules
• 6.5.2 Effect of Confidence Preference the Predicted Accuracy
• 6.5.3 Effectiveness Comparison
• 6.6 Conclusion
• References
• 7 Recency-based Updating and Dynamic Management of Contextual
• Rules
• 7.1 Introduction
• 7.2 Requirements Analysis
• 7.3 An Example of Recent Data
• 7.4 Identifying Optimal Period of Recent Log Data
• 7.4.1 Data Splitting
• 7.4.2 Association Generation
• 7.4.3 Score Calculation
• 7.4.4 Data Aggregation
• 7.5 Machine Learning based Behavioral Rule Generation and Management
• 7.6 Effectiveness Comparison and Analysis
• 7.7 Conclusion
• References
• Part III Application and Deep Learning Perspective
• 8 Context-Aware Rule-based Expert System Modeling
• 8.1 Structure of a Context-Aware Mobile Expert System
• 8.2 Context-Aware Rule Generation Methods
• 8.3 Context-Aware IF-THEN Rules and Discussion
• 8.3.1 IF-THEN Classification Rules
• 8.3.2 IF-THEN Association Rules
• x Contents
• 8.4 Conclusion
• References
• 9 Deep Learning for Contextual Mobile Data Analytics
• 9.1 Introduction
• 9.2 Contextual Data
• 9.3 Deep Neural Network Modeling
• 9.3.1 Model Overview
• 9.3.2 Input Layer
• 9.3.3 Hidden Layer(s)
• 9.3.4 Output Layer
• 9.4 Prediction Results of the Model
• 9.5 Conclusion
• References
• 10 Context-Aware Machine Learning System: Applications and
• Challenging Issues
• 10.1 Rule-based Intelligent Mobile Applications
• 10.2 Major Challenges and Research Issues
• 10.3 Concluding Remarks
• References
• .
• (source: Nielsen Book Data)

This book offers a clear understanding of the concept of context-aware machine learning including an automated rule-based framework within the broad area of data science and analytics, particularly, with the aim of data-driven intelligent decision making. Thus, we have bestowed a comprehensive study on this topic that explores multi-dimensional contexts in machine learning modeling, context discretization with time-series modeling, contextual rule discovery and predictive analytics, recent-pattern or rule-based behavior modeling, and their usefulness in various context-aware intelligent applications and services. The presented machine learning-based techniques can be employed in a wide range of real-world application areas ranging from personalized mobile services to security intelligence, highlighted in the book. As the interpretability of a rule-based system is high, the automation in discovering rules from contextual raw data can make this book more impactful for the application developers as well as researchers. Overall, this book provides a good reference for both academia and industry people in the broad area of data science, machine learning, AI-Driven computing, human-centered computing and personalization, behavioral analytics, IoT and mobile applications, and cybersecurity intelligence.
(source: Nielsen Book Data)

• Preface
• 1. Introduction
• 2. Probability and information --3. Linear algebra and optimization
• 4. Linear and nonlinear regression
• 5. Peceptron
• 6. Multilayer perceptron
• 7. Learning theory
• 8. Model selection
• 9. Clustering
• 10. Radial basis networks
• 11. Support vector machines
• 12. Deep learning
• 13. Convolutional networks
• 14. Recurrent networks
• 15. Autoencoders
• 16. Epilogue
• Bibliography
• Index

This unique compendium discusses some core ideas for the development and implementation of machine learning from three different perspectives - the statistical perspective, the artificial neural network perspective and the deep learning methodology.The useful reference text represents a solid foundation in machine learning and should prepare readers to apply and understand machine learning algorithms as well as to invent new machine learning methods. It tells a story outgoing from a perceptron to deep learning highlighted with concrete examples, including exercises and answers for the students.
(source: Nielsen Book Data)

• Chapter 1. Introduction
• Chapter 2. Pairs Trading - Background and Related Work
• Chapter 3. Proposed Pairs Selection Framework
• Chapter 4. Proposed Trading Model
• Chapter 5. Implementation
• Chapter 6. Results
• Chapter 7. Conclusions and Future Work.
• (source: Nielsen Book Data)

This book investigates the application of promising machine learning techniques to address two problems: (i) how to find profitable pairs while constraining the search space and (ii) how to avoid long decline periods due to prolonged divergent pairs. It also proposes the integration of an unsupervised learning algorithm, OPTICS, to handle problem (i), and demonstrates that the suggested technique can outperform the common pairs search methods, achieving an average portfolio Sharpe ratio of 3.79, in comparison to 3.58 and 2.59 obtained using standard approaches. For problem (ii), the authors introduce a forecasting-based trading model capable of reducing the periods of portfolio decline by 75%. However, this comes at the expense of decreasing overall profitability. The authors also test the proposed strategy using an ARMA model, an LSTM and an LSTM encoder-decoder.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Mathematical Foundation
• 3. Supervised Machine Learning (in a nutshell)
• 4. Feature Extraction
• 5. Statistical Learning Theory
• 6. Linear Models
• 7. Learning Discriminative Models in General
• 8. Neural Networks
• 9. Ensemble Learning
• 10. Overview of Generative Models
• 11. Unimodal Models
• 12. Mixture Models
• 13. Entangled Models
• 14. Bayesian Learning
• 15. Graphical Models.
• (source: Nielsen Book Data)

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely "from scratch" based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.
(source: Nielsen Book Data)