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Today, they can generate ideas, write stories, translate languages, and even hold conversations that feel almost human. Behind this leap forward are large language models (LLMs)-the technology powering tools like ChatGPT and reshaping how we work, learn, and communicate.  Issam El Naqa, "Machine and Deep Learning in Oncology, Medical Physics and Radiology" English | ISBN: 3030830462 | 2022 | 529 pages | AZW3 | 15 MB This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members ofapplied machine learning communities.  Machine Unlearning by Ajit Singh English | September 30, 2025 | ISBN: N/A | ASIN: B0FTFVCRXT | 256 pages | EPUB | 2.45 Mb This book, Machine Unlearning for Technical Developers and AI Researchers, is designed to bridge the gap between theoretical research and practical implementation. It provides a comprehensive exploration of Machine Unlearning, covering foundational concepts, algorithmic approaches, real-world applications, and emerging challenges. The book is structured to cater to both practitioners and researchers, offering rigorous mathematical formulations, hands-on implementation techniques, and insights into legal and ethical considerations.  Machine Translation and Translation Theory by Omri Asscher English | July 17, 2025 | ISBN: 1041000669 | 178 pages | MOBI | 0.57 Mb Pervasive and ubiquitous, machine translation systems have been transforming communication and understanding across languages and cultures on a historical scale. Focused on both Neural Machine Translation tools, such as Google Translate, and generative AI tools, such as ChatGPT, Omri Asscher pursues the juncture between machine translation and the diverse, often competing, frameworks of human translation theory. He shines a light on the subtleties of the intersection between the two: the places where machine translation corresponds well with the ideas that have been developed on human translation throughout the years, and the places where machine translation seems to challenge translation theory, and perhaps even require that we rethink some of its basic assumptions.  Machine Learning with Scikit-Learn and TensorFlow: A Hands-On Guide to Scikit-Learn and TensorFlow for Real World AI English | 7 Dec. 2025 | ASIN: B0G5K2SZN9 | 146 pages | Epub | 1.46 MB Machine Learning with Scikit-Learn and TensorFlow: A Hands-On Guide to Scikit-Learn and TensorFlow for Real World AI This book is the definitive, hands-on guide for developers and data scientists looking to master the end-to-end Machine Learning pipeline. Starting with the foundational principles of data representation, statistics, and optimization (calculus, gradient descent), the book provides a comprehensive journey across the entire ML landscape. Part I focuses on classical methods using Scikit-Learn, covering linear models, evaluation metrics (ROC, AUC, F1-Score), Support Vector Machines, and powerful ensemble techniques like Random Forests and Gradient Boosting. Part II shifts entirely to Deep Learning with TensorFlow and Keras, tackling the instability of deep networks (vanishing/exploding gradients) using modern solutions like Batch Normalization and Transfer Learning. Readers will learn to architect specialized networks, including Convolutional Neural Networks (CNNs) for vision, Recurrent Neural Networks (RNNs) for sequence processing, and Generative Adversarial Networks (GANs) for creating new data. The final section addresses production readiness, detailing scalable data pipelines (tf.data), distributed training strategies, and deployment using the SavedModel format, TensorFlow Serving, and TensorFlow Lite for edge devices. This guide ensures practitioners can not only build sophisticated models but also deploy and monitor them reliably at scale.  Machine Learning with Python: A Beginner-Friendly Guide to Building Real-World ML Models (The CodeCraft Series) English | December 7, 2025 | ASIN: B0G5K3BGSZ | 378 pages | Epub | 1.94 MB Machine Learning with Python: A Beginner-Friendly Guide to Building Real-World ML Models is your step-by-step roadmap to mastering machine learning from the ground up. Designed for beginners and aspiring data scientists, this hands-on guide teaches you how to build powerful, real-world machine learning models using Python , Scikit-Learn , NumPy , Pandas , and Jupyter Notebooks . You'll learn how to clean and prepare data, perform exploratory data analysis, build regression and classification models, apply clustering and dimensionality reduction techniques, and deploy models using modern tools like Flask and FastAPI, all through practical, project-based learning. Inside this book, you'll go beyond theory and start building smarter systems that think, learn, and predict. Whether your goal is to become a machine learning engineer, data scientist, or AI developer, this guide gives you the skills, confidence, and real-world experience to succeed. If you're ready to unlock the power of machine learning and start creating intelligent applications today, scroll up and click "Buy Now" to begin your journey! 🚀  Machine Learning in Healthcare: Advances and Future Prospects by Rishabha Malviya, Niranjan Kaushik, Tamanna Rai English | September 14, 2025 | ISBN: 1779640005 | 150 pages | MOBI | 4.56 Mb This new volume explores the integration of machine learning in healthcare, which has transformed technology for disease diagnosis, treatment, and management. The book shows the enormous possibilities made possible by computational technologies, ranging from analyzing electronic health information to predicting, detecting, and treating cancer, cardiovascular disease, thyroid disorders, and diabetes. The exploration extends beyond conventional domains, discussing topics such as wearable devices and mental health management through the use of machine learning technology.  Machine Learning for Volatility Forecasting: LSTMs, Transformers, and Regime Models: Deep Learning Models for Realized Volatility, Implied Vol Surfaces, and Regime-Switching Risk in Python by James Preston, Danny Munrow English | September 18, 2025 | ISBN: N/A | ASIN: B0FRRDZVK9 | 681 pages | EPUB | 0.60 Mb Reactive Publishing  Machine Learning for Engineers by Ajit Singh English | July 16, 2025 | ISBN: N/A | ASIN: B0FHWHV3J6 | 234 pages | EPUB | 0.23 Mb "Machine Learning for Engineers" is a foundational textbook meticulously crafted to introduce B.Tech and M.Tech engineering students to the principles and practices of Machine Learning (ML). This book serves as a bridge, connecting the theoretical underpinnings of ML algorithms with their practical application in solving complex engineering problems. Recognizing that the engineers of tomorrow must be adept at leveraging data, this book demystifies ML, making it accessible, intuitive, and directly relevant to their discipline.  Jitendra Kumar, "Machine Learning for Cloud Management" English | ISBN: 0367626489 | 2021 | 198 pages | MOBI | 13 MB Cloud computing offers subscription-based on-demand services, and it has emerged as the backbone of the computing industry. It has enabled us to share resources among multiple users through virtualization, which creates a virtual instance of a computer system running in an abstracted hardware layer. Unlike early distributed computing models, it offers virtually limitless computing resources through its large scale cloud data centers. It has gained wide popularity over the past few years, with an ever-increasing infrastructure, a number of users, and the amount of hosted data. The large and complex workloads hosted on these data centers introduce many challenges, including resource utilization, power consumption, scalability, and operational cost. Therefore, an effective resource management scheme is essential to achieve operational efficiency with improved elasticity. 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