Free Download Advances in Metaheuristic Algorithms for Optimal Design of Structures, Third Edition by Ali Kaveh
English | EPUB | 2021 | 890 Pages | ISBN : 3030593916 | 163.5 MB
This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his graduate students, consisting of Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Democratic Particle Swarm Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization.

Free Download Python-Based Data Structures and Algorithms
English | 2024 | ISBN: 9798223165385 | 1071 Pages | EPUB (True) | 9 MB
"Python-Based Data Structures and Algorithms" is a comprehensive guide to mastering the fundamental concepts of algorithms and data structures using the Python programming language. Covering a wide spectrum, from algorithm analysis and problem complexity to computational complexity, this book equips readers with the essential skills for effective programming. Whether you're a novice or an experienced developer, this resource provides practical insights into implementing algorithms and data structures in Python. With a focus on clarity and efficiency, it's an invaluable reference for anyone looking to enhance their programming proficiency and tackle real-world challenges in algorithmic problem-solving.

Free Download Optimization Algorithms (MEAP V10)
English | 2023 | ISBN: 9781633438835 | 1011 Pages | PDF EPUB | 104 MB
Optimization problems are everywhere in daily life. What's the fastest route from one place to another? How do you calculate the optimal price for a product? How should you plant crops, allocate resources, and schedule surgeries? Optimization Algorithms introduces the AI algorithms that can solve these complex and poorly-structured problems. Inside you'll find a wide range of optimization methods, from deterministic and stochastic derivative-free optimization to nature-inspired search algorithms and machine learning methods. Don't worry-there's no complex mathematical notation. You'll learn through in-depth case studies that cut through academic complexity to demonstrate how each algorithm works in the real world.

Free Download Giuseppe Bonaccorso, "Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition"
English | 2018 | pages: 514 | ISBN: 1789347998 | PDF | 65,3 mb
An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms

Free Download Graph Algorithms for Data Science (MEAP V09)
English | 2023 | ISBN: 9781617299469 | 432 Pages | PDF EPUB | 22 MB
Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. You don't need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects.

Free Download Algorithms for Measurement Invariance Testing: Contrasts and Connections
English | 2023 | ISBN: 100945417X | 94 Pages | PDF (True) | 4 MB
Latent variable models are a powerful tool for measuring many of the phenomena in which developmental psychologists are often interested. If these phenomena are not measured equally well among all participants, this would result in biased inferences about how they unfold throughout development. In the absence of such biases, measurement invariance is achieved; if this bias is present, differential item functioning (DIF) would occur. This Element introduces the testing of measurement invariance/DIF through nonlinear factor analysis. After introducing models which are used to study these questions, the Element uses them to formulate different definitions of measurement invariance and DIF. It also focuses on different procedures for locating and quantifying these effects. The Element finally provides recommendations for researchers about how to navigate these options to make valid inferences about measurement in their own data.

Free Download Kenneth. J Goldman, "A Practical Guide to Data Structures and Algorithms using Java"
English | 2008 | pages: 1056 | ISBN: 158488455X | PDF | 21,3 mb
Although traditional texts present isolated algorithms and data structures, they do not provide a unifying structure and offer little guidance on how to appropriately select among them. Furthermore, these texts furnish little, if any, source code and leave many of the more difficult aspects of the implementation as exercises. A fresh alternative to conventional data structures and algorithms books, A Practical Guide to Data Structures and Algorithms using Java presents comprehensive coverage of fundamental data structures and algorithms in a unifying framework with full implementation details. Recognizing that software development is a top-down process, this applications-centered book provides careful guidance to students and practitioners.

Free Download Feminist AI
by Browne, Jude;Cave, Stephen;Drage, Eleanor;McInerney, Kerry;
English | 2023 | ISBN: 0192889893 | 433 pages | True PDF EPUB | 17.54 MB

Free Download Binary Neural Networks
by Zhang, Baochang;Xu, Sheng;Lin, Mingbao;Wang, Tiancheng;Doermann, David;, Xu, Sheng, Lin, Mingbao, Wang, Tiancheng, Doermann, David
English | 2024 | ISBN: 103245248X | 203 pages | True EPUB | 7.09 MB

Free Download Parallel Operator Splitting Algorithms with Application to Imaging Inverse Problems
English | 2023 | ISBN: 9819937493 | 345 Pages | PDF EPUB (True) | 50 MB
Image denoising, image deblurring, image inpainting, super-resolution, and compressed sensing reconstruction have important application value in engineering practice, and they are also the hot frontiers in the field of image processing. This book focuses on the numerical analysis of ill condition of imaging inverse problems and the methods of solving imaging inverse problems based on operator splitting. Both algorithmic theory and numerical experiments have been addressed. The book is divided into six chapters, including preparatory knowledge, ill-condition numerical analysis and regularization method of imaging inverse problems, adaptive regularization parameter estimation, and parallel solution methods of imaging inverse problem based on operator splitting. Although the research methods in this book take image denoising, deblurring, inpainting, and compressed sensing reconstruction as examples, they can also be extended to image processing problems such as image segmentation, hyperspectral decomposition, and image compression. This book can benefit teachers and graduate students in colleges and universities, or be used as a reference for self-study or further study of image processing technology engineers.