The Art Of Optimization Mastering The Cross Entropy Method
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The Art Of Optimization Mastering The Cross Entropy Method
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Author : Pasquale De Marco
language : en
Publisher: Pasquale De Marco
Release Date :
The Art Of Optimization Mastering The Cross Entropy Method written by Pasquale De Marco and has been published by Pasquale De Marco this book supported file pdf, txt, epub, kindle and other format this book has been release on with Technology & Engineering categories.
In the realm of optimization, where efficiency, precision, and innovation converge, lies a transformative method that has redefined problem-solving: the Cross-Entropy Method. This book is your gateway to mastering this groundbreaking technique and unlocking its potential to revolutionize your approach to complex optimization challenges. Written with clarity and rigor, this comprehensive guide provides a thorough exploration of the Cross-Entropy Method, unraveling its theoretical foundations and delving into its practical applications. Whether you are a seasoned optimization practitioner or embarking on your optimization journey, this book will equip you with the knowledge and skills to conquer even the most daunting optimization problems. With a focus on accessibility and real-world relevance, this book takes you on a hands-on journey through the intricacies of the Cross-Entropy Method. You will gain a deep understanding of its algorithmic framework, parameter settings, and convergence properties. Expert guidance and illustrative examples accompany you every step of the way, ensuring that you can harness the full power of this remarkable method. The applications of the Cross-Entropy Method span a vast array of fields, from machine learning and artificial intelligence to finance, engineering, and healthcare. This book explores these diverse applications in depth, showcasing how the Cross-Entropy Method has revolutionized industries and disciplines. In the realm of machine learning, you will discover how the Cross-Entropy Method empowers algorithms to learn from data with remarkable accuracy. In the financial world, you will learn how it optimizes portfolios, models market behavior, and guides algorithmic trading strategies. The book also delves into the applications of the Cross-Entropy Method in engineering and design, where it enhances the performance and efficiency of structures, systems, and products. You will uncover its contributions to healthcare and medicine, where it accelerates drug discovery, optimizes medical imaging, and personalizes treatments. With its comprehensive coverage, clear explanations, and practical examples, this book is an indispensable resource for anyone seeking to master the art of optimization. Unlock the power of the Cross-Entropy Method and transform your approach to problem-solving today! If you like this book, write a review!
The Cross Entropy Method
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Author : Reuven Y. Rubinstein
language : en
Publisher: Springer Science & Business Media
Release Date : 2004-07-28
The Cross Entropy Method written by Reuven Y. Rubinstein 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 2004-07-28 with Business & Economics categories.
Rubinstein is the pioneer of the well-known score function and cross-entropy methods. Accessible to a broad audience of engineers, computer scientists, mathematicians, statisticians and in general anyone, theorist and practitioner, who is interested in smart simulation, fast optimization, learning algorithms, and image processing.
Mastering Large Language Models
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Author : Sanket Subhash Khandare
language : en
Publisher: BPB Publications
Release Date : 2024-03-12
Mastering Large Language Models written by Sanket Subhash Khandare and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-12 with Computers categories.
Do not just talk AI, build it: Your guide to LLM application development KEY FEATURES ● Explore NLP basics and LLM fundamentals, including essentials, challenges, and model types. ● Learn data handling and pre-processing techniques for efficient data management. ● Understand neural networks overview, including NN basics, RNNs, CNNs, and transformers. ● Strategies and examples for harnessing LLMs. DESCRIPTION Transform your business landscape with the formidable prowess of large language models (LLMs). The book provides you with practical insights, guiding you through conceiving, designing, and implementing impactful LLM-driven applications. This book explores NLP fundamentals like applications, evolution, components and language models. It teaches data pre-processing, neural networks , and specific architectures like RNNs, CNNs, and transformers. It tackles training challenges, advanced techniques such as GANs, meta-learning, and introduces top LLM models like GPT-3 and BERT. It also covers prompt engineering. Finally, it showcases LLM applications and emphasizes responsible development and deployment. With this book as your compass, you will navigate the ever-evolving landscape of LLM technology, staying ahead of the curve with the latest advancements and industry best practices. WHAT YOU WILL LEARN ● Grasp fundamentals of natural language processing (NLP) applications. ● Explore advanced architectures like transformers and their applications. ● Master techniques for training large language models effectively. ● Implement advanced strategies, such as meta-learning and self-supervised learning. ● Learn practical steps to build custom language model applications. WHO THIS BOOK IS FOR This book is tailored for those aiming to master large language models, including seasoned researchers, data scientists, developers, and practitioners in natural language processing (NLP). TABLE OF CONTENTS 1. Fundamentals of Natural Language Processing 2. Introduction to Language Models 3. Data Collection and Pre-processing for Language Modeling 4. Neural Networks in Language Modeling 5. Neural Network Architectures for Language Modeling 6. Transformer-based Models for Language Modeling 7. Training Large Language Models 8. Advanced Techniques for Language Modeling 9. Top Large Language Models 10. Building First LLM App 11. Applications of LLMs 12. Ethical Considerations 13. Prompt Engineering 14. Future of LLMs and Its Impact
Mastering Neural Networks
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Author : Cybellium
language : en
Publisher: Cybellium Ltd
Release Date :
Mastering Neural Networks written by Cybellium and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Unleash the Power of Deep Learning for Intelligent Systems In the realm of artificial intelligence and machine learning, neural networks stand as the driving force behind intelligent systems that mimic human cognition. "Mastering Neural Networks" is your ultimate guide to comprehending and harnessing the potential of these powerful algorithms, empowering you to create intelligent solutions that push the boundaries of innovation. About the Book: As technology advances, the capabilities of neural networks become more integral to various fields. "Mastering Neural Networks" offers an in-depth exploration of this cutting-edge subject—an essential toolkit for data scientists, engineers, and enthusiasts. This book caters to both newcomers and experienced learners aiming to excel in neural network concepts, architectures, and applications. Key Features: Neural Network Fundamentals: Begin by understanding the core principles of neural networks. Learn about artificial neurons, activation functions, and the architecture of these powerful algorithms. Feedforward Neural Networks: Dive into feedforward neural networks. Explore techniques for designing, training, and optimizing networks for various tasks. Convolutional Neural Networks: Grasp the art of convolutional neural networks. Understand how these architectures excel in image and pattern recognition tasks. Recurrent Neural Networks: Explore recurrent neural networks. Learn how to process sequences and time-series data, making them suitable for tasks like language modeling and speech recognition. Generative Adversarial Networks: Understand the significance of generative adversarial networks. Explore how these networks enable the generation of realistic images, text, and data. Transfer Learning and Fine-Tuning: Delve into transfer learning. Learn how to leverage pretrained models and adapt them to new tasks, saving time and resources. Neural Network Optimization: Grasp optimization techniques. Explore methods for improving network performance, reducing overfitting, and tuning hyperparameters. Real-World Applications: Gain insights into how neural networks are applied across industries. From healthcare to finance, discover the diverse applications of these algorithms. Why This Book Matters: In a world driven by intelligent systems, mastering neural networks offers a competitive advantage. "Mastering Neural Networks" empowers data scientists, engineers, and technology enthusiasts to leverage these cutting-edge algorithms, enabling them to create intelligent solutions that redefine the boundaries of innovation. Unleash the Future of Intelligence: In the landscape of artificial intelligence, neural networks are reshaping technology and innovation. "Mastering Neural Networks" equips you with the knowledge needed to leverage these powerful algorithms, enabling you to create intelligent solutions that push the boundaries of innovation and redefine what's possible. Whether you're a seasoned practitioner or new to the world of neural networks, this book will guide you in building a solid foundation for effective AI-driven solutions. Your journey to mastering neural networks starts here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
Machine Learning For Beginners 2025 Step By Step Guide To Master Ml Algorithms Real World Applications
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Author : J. Paaul
language : en
Publisher: Code Academy
Release Date : 2025-05-07
Machine Learning For Beginners 2025 Step By Step Guide To Master Ml Algorithms Real World Applications written by J. Paaul and has been published by Code Academy this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-07 with Computers categories.
Machine Learning for Beginners 2025 is the perfect guide for anyone looking to dive into the world of machine learning. This book breaks down complex concepts into easy-to-understand explanations and hands-on examples. Covering the fundamentals of ML algorithms, data preprocessing, model evaluation, and real-world applications, this book is ideal for newcomers to the field. With practical projects and step-by-step tutorials, readers will gain the skills to implement machine learning models using Python and popular libraries like Scikit-learn and TensorFlow, making this a comprehensive resource for aspiring data scientists.
Deep Learning
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Author : Dengsheng Zhang
language : en
Publisher: ZDS Online Publishing
Release Date :
Deep Learning written by Dengsheng Zhang and has been published by ZDS Online Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
This book aims to help readers have a systematic understanding of deep learning technology through practical systems and develop their own strategies on network design. To achieve this goal, the book adopts a diagnostic and prescriptive approach. The book starts with breaking down a canonical deep learning network into blocks and layers to understand the complexity and behavior of the network, bottlenecks and issues are identified as a result. A series of advanced network engineering methods are presented targeting specific issues in deep learning design. Those methods include recurrent convolutional neural network, residual convolutional neural networks, 1x1 transformation, autoencoder, U-nets, graph convolution network, region-based convolutional neural networks, YOLO object detection network, backpropagation and generative adversarial networks.
Deep Learning For Computer Vision In The Art Domain
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Author : Christian Bartz
language : en
Publisher: Universitätsverlag Potsdam
Release Date : 2021-11-15
Deep Learning For Computer Vision In The Art Domain written by Christian Bartz and has been published by Universitätsverlag Potsdam this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-15 with Computers categories.
In recent years, computer vision algorithms based on machine learning have seen rapid development. In the past, research mostly focused on solving computer vision problems such as image classification or object detection on images displaying natural scenes. Nowadays other fields such as the field of cultural heritage, where an abundance of data is available, also get into the focus of research. In the line of current research endeavours, we collaborated with the Getty Research Institute which provided us with a challenging dataset, containing images of paintings and drawings. In this technical report, we present the results of the seminar "Deep Learning for Computer Vision". In this seminar, students of the Hasso Plattner Institute evaluated state-of-the-art approaches for image classification, object detection and image recognition on the dataset of the Getty Research Institute. The main challenge when applying modern computer vision methods to the available data is the availability of annotated training data, as the dataset provided by the Getty Research Institute does not contain a sufficient amount of annotated samples for the training of deep neural networks. However, throughout the report we show that it is possible to achieve satisfying to very good results, when using further publicly available datasets, such as the WikiArt dataset, for the training of machine learning models.
Convex Optimization
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Author : Stephen P. Boyd
language : en
Publisher: Cambridge University Press
Release Date : 2004-03-08
Convex Optimization written by Stephen P. Boyd 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 2004-03-08 with Business & Economics categories.
Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.
Algorithms For Optimization
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Author : Mykel J. Kochenderfer
language : en
Publisher: MIT Press
Release Date : 2019-03-12
Algorithms For Optimization written by Mykel J. Kochenderfer and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-12 with Computers categories.
A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.
Entropy Methods For Diffusive Partial Differential Equations
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Author : Ansgar Jüngel
language : en
Publisher: Springer
Release Date : 2016-06-17
Entropy Methods For Diffusive Partial Differential Equations written by Ansgar Jüngel and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-17 with Mathematics categories.
This book presents a range of entropy methods for diffusive PDEs devised by many researchers in the course of the past few decades, which allow us to understand the qualitative behavior of solutions to diffusive equations (and Markov diffusion processes). Applications include the large-time asymptotics of solutions, the derivation of convex Sobolev inequalities, the existence and uniqueness of weak solutions, and the analysis of discrete and geometric structures of the PDEs. The purpose of the book is to provide readers an introduction to selected entropy methods that can be found in the research literature. In order to highlight the core concepts, the results are not stated in the widest generality and most of the arguments are only formal (in the sense that the functional setting is not specified or sufficient regularity is supposed). The text is also suitable for advanced master and PhD students and could serve as a textbook for special courses and seminars.