Soft Computing and Machine Learning with Python

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Publisher : Arcler Press
ISBN 13 : 9781773615004
Total Pages : 0 pages
Book Rating : 4.6/5 (15 download)

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Book Synopsis Soft Computing and Machine Learning with Python by : Zoran Gacovski

Download or read book Soft Computing and Machine Learning with Python written by Zoran Gacovski and published by Arcler Press. This book was released on 2018-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A definition states that the machine learning is a discipline that allows the computers to learn without explicit programming. The challenge in machine learning is how to accurately (algorithmic) describe some kinds of tasks that people can easily solve (for example face recognition, speech recognition etc.). Such algorithms can be defined for certain types of tasks, but they are very complex and/or require large knowledge base (e.g. machine translation MT). In many of the areas - data are continuously collected in order to get "some knowledge out of them" for example - in medicine (patient data and therapy), in marketing (the users / customers and what they buy, what are they interested in, how products are rated etc.). Data analysis of this scale requires approaches that will allow you to discover patterns and dependences among the data, that are neither known, nor obvious, but can be useful (data mining). Information retrieval - IR, is finding existing information as quickly as possible. For example, web browser - finds page within the (large) set of the entire WWW. Machine Learning - ML, is a set of techniques that generalize existing knowledge of the new information, as precisely as possible. An example is the speech recognition. Data mining - DM, primarily relates to the disclosure of something hidden within the data, some new dependence, which have not previously been known. Example is CRM - the customer analysis. Python is high-level programming language that is very suitable for web development, programming of games, and data manipulation / machine learning applications. It is object-oriented language and interpreter as well, allowing the source code to execute directly (without compiling). This edition covers machine learning theory and applications with Python, and includes chapters for soft computing theory, machine learning techniques/applications, Python language details, and machine learning examples with Python. Book jacket.

Deep Learning with Python

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Publisher : Simon and Schuster
ISBN 13 : 1638352046
Total Pages : 597 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Deep Learning with Python by : Francois Chollet

Download or read book Deep Learning with Python written by Francois Chollet and published by Simon and Schuster. This book was released on 2017-11-30 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance

Principles of Soft Computing Using Python Programming

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Author :
Publisher : John Wiley & Sons
ISBN 13 : 1394173156
Total Pages : 356 pages
Book Rating : 4.3/5 (941 download)

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Book Synopsis Principles of Soft Computing Using Python Programming by : Gypsy Nandi

Download or read book Principles of Soft Computing Using Python Programming written by Gypsy Nandi and published by John Wiley & Sons. This book was released on 2023-11-28 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principles of Soft Computing Using Python Programming An accessible guide to the revolutionary techniques of soft computing Soft computing is a computing approach designed to replicate the human mind’s unique capacity to integrate uncertainty and imprecision into its reasoning. It is uniquely suited to computing operations where rigid analytical models will fail to account for the variety and ambiguity of possible solutions. As machine learning and artificial intelligence become more and more prominent in the computing landscape, the potential for soft computing techniques to revolutionize computing has never been greater. Principles of Soft Computing Using Python Programming provides readers with the knowledge required to apply soft computing models and techniques to real computational problems. Beginning with a foundational discussion of soft or fuzzy computing and its differences from hard computing, it describes different models for soft computing and their many applications, both demonstrated and theoretical. The result is a set of tools with the potential to produce new solutions to the thorniest computing problems. Readers of Principles of Soft Computing Using Python Programming will also find: Each chapter accompanied with Python codes and step-by-step comments to illustrate applications Detailed discussion of topics including artificial neural networks, rough set theory, genetic algorithms, and more Exercises at the end of each chapter including both short- and long-answer questions to reinforce learning Principles of Soft Computing Using Python Programming is ideal for researchers and engineers in a variety of fields looking for new solutions to computing problems, as well as for advanced students in programming or the computer sciences.

Python Machine Learning Projects

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Author :
Publisher : DigitalOcean
ISBN 13 : 099977302X
Total Pages : 152 pages
Book Rating : 4.9/5 (997 download)

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Book Synopsis Python Machine Learning Projects by : Lisa Tagliaferri

Download or read book Python Machine Learning Projects written by Lisa Tagliaferri and published by DigitalOcean. This book was released on 2019-05-02 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: As machine learning is increasingly leveraged to find patterns, conduct analysis, and make decisions — sometimes without final input from humans who may be impacted by these findings — it is crucial to invest in bringing more stakeholders into the fold. This book of Python projects in machine learning tries to do just that: to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning to help ensure that it is serving us all. This book will set you up with a Python programming environment if you don’t have one already, then provide you with a conceptual understanding of machine learning in the chapter “An Introduction to Machine Learning.” What follows next are three Python machine learning projects. They will help you create a machine learning classifier, build a neural network to recognize handwritten digits, and give you a background in deep reinforcement learning through building a bot for Atari.

Foundations of Machine Learning, second edition

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Publisher : MIT Press
ISBN 13 : 0262351366
Total Pages : 505 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Foundations of Machine Learning, second edition by : Mehryar Mohri

Download or read book Foundations of Machine Learning, second edition written by Mehryar Mohri and published by MIT Press. This book was released on 2018-12-25 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.

NEURAL NETWORKS, FUZZY LOGIC AND GENETIC ALGORITHM

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Publisher : PHI Learning Pvt. Ltd.
ISBN 13 : 8120321863
Total Pages : 459 pages
Book Rating : 4.1/5 (23 download)

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Book Synopsis NEURAL NETWORKS, FUZZY LOGIC AND GENETIC ALGORITHM by : S. RAJASEKARAN

Download or read book NEURAL NETWORKS, FUZZY LOGIC AND GENETIC ALGORITHM written by S. RAJASEKARAN and published by PHI Learning Pvt. Ltd.. This book was released on 2003-01-01 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides comprehensive introduction to a consortium of technologies underlying soft computing, an evolving branch of computational intelligence. The constituent technologies discussed comprise neural networks, fuzzy logic, genetic algorithms, and a number of hybrid systems which include classes such as neuro-fuzzy, fuzzy-genetic, and neuro-genetic systems. The hybridization of the technologies is demonstrated on architectures such as Fuzzy-Back-propagation Networks (NN-FL), Simplified Fuzzy ARTMAP (NN-FL), and Fuzzy Associative Memories. The book also gives an exhaustive discussion of FL-GA hybridization. Every architecture has been discussed in detail through illustrative examples and applications. The algorithms have been presented in pseudo-code with a step-by-step illustration of the same in problems. The applications, demonstrative of the potential of the architectures, have been chosen from diverse disciplines of science and engineering. This book with a wealth of information that is clearly presented and illustrated by many examples and applications is designed for use as a text for courses in soft computing at both the senior undergraduate and first-year post-graduate engineering levels. It should also be of interest to researchers and technologists desirous of applying soft computing technologies to their respective fields of work.

Deep Learning with Keras

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Publisher : Packt Publishing Ltd
ISBN 13 : 1787129039
Total Pages : 310 pages
Book Rating : 4.7/5 (871 download)

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Book Synopsis Deep Learning with Keras by : Antonio Gulli

Download or read book Deep Learning with Keras written by Antonio Gulli and published by Packt Publishing Ltd. This book was released on 2017-04-26 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to grips with the basics of Keras to implement fast and efficient deep-learning models About This Book Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning models and practical use-cases can be implemented using Keras A practical, hands-on guide with real-world examples to give you a strong foundation in Keras Who This Book Is For If you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this book a useful entry point to deep-learning with Keras. A knowledge of Python is required for this book. What You Will Learn Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm Fine-tune a neural network to improve the quality of results Use deep learning for image and audio processing Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases Identify problems for which Recurrent Neural Network (RNN) solutions are suitable Explore the process required to implement Autoencoders Evolve a deep neural network using reinforcement learning In Detail This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks. Style and approach This book is an easy-to-follow guide full of examples and real-world applications to help you gain an in-depth understanding of Keras. This book will showcase more than twenty working Deep Neural Networks coded in Python using Keras.

Rough-Neural Computing

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Publisher : Springer Science & Business Media
ISBN 13 : 3642188591
Total Pages : 741 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Rough-Neural Computing by : Sankar Kumar Pal

Download or read book Rough-Neural Computing written by Sankar Kumar Pal and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 741 pages. Available in PDF, EPUB and Kindle. Book excerpt: Soft computing comprises various paradigms dedicated to approximately solving real-world problems, e.g. in decision making, classification or learning; among these paradigms are fuzzy sets, rough sets, neural networks, genetic algorithms, and others. It is well understood now in the soft computing community that hybrid approaches combining various paradigms are very promising approaches for solving complex problems. Exploiting the potential and strength of both neural networks and rough sets, this book is devoted to rough-neuro computing which is also related to the novel aspect of computing based on information granulation, in particular to computing with words. It provides foundational and methodological issues as well as applications in various fields.

Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications

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Publisher : Springer Nature
ISBN 13 : 3031639294
Total Pages : 254 pages
Book Rating : 4.0/5 (316 download)

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Book Synopsis Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications by : Nguyen Hoang Phuong

Download or read book Machine Learning and Other Soft Computing Techniques: Biomedical and Related Applications written by Nguyen Hoang Phuong and published by Springer Nature. This book was released on with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Deep Neuro-Fuzzy Systems with Python

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Publisher : Apress
ISBN 13 : 1484253612
Total Pages : 270 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Deep Neuro-Fuzzy Systems with Python by : Himanshu Singh

Download or read book Deep Neuro-Fuzzy Systems with Python written by Himanshu Singh and published by Apress. This book was released on 2019-11-30 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain insight into fuzzy logic and neural networks, and how the integration between the two models makes intelligent systems in the current world. This book simplifies the implementation of fuzzy logic and neural network concepts using Python. You’ll start by walking through the basics of fuzzy sets and relations, and how each member of the set has its own membership function values. You’ll also look at different architectures and models that have been developed, and how rules and reasoning have been defined to make the architectures possible. The book then provides a closer look at neural networks and related architectures, focusing on the various issues neural networks may encounter during training, and how different optimization methods can help you resolve them. In the last section of the book you’ll examine the integrations of fuzzy logics and neural networks, the adaptive neuro fuzzy Inference systems, and various approximations related to the same. You’ll review different types of deep neuro fuzzy classifiers, fuzzy neurons, and the adaptive learning capability of the neural networks. The book concludes by reviewing advanced neuro fuzzy models and applications. What You’ll Learn Understand fuzzy logic, membership functions, fuzzy relations, and fuzzy inferenceReview neural networks, back propagation, and optimizationWork with different architectures such as Takagi-Sugeno model, Hybrid model, genetic algorithms, and approximations Apply Python implementations of deep neuro fuzzy system Who This book Is For Data scientists and software engineers with a basic understanding of Machine Learning who want to expand into the hybrid applications of deep learning and fuzzy logic.

Soft Computing Applications

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Publisher : Springer Nature
ISBN 13 : 303123636X
Total Pages : 750 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Soft Computing Applications by : Valentina Emilia Balas

Download or read book Soft Computing Applications written by Valentina Emilia Balas and published by Springer Nature. This book was released on 2023-10-26 with total page 750 pages. Available in PDF, EPUB and Kindle. Book excerpt: Soft computing techniques open significant opportunities in several areas, such as industry, medicine, energy, security, transportation, and education. This book provides theory and applications development using soft computing techniques by organizing intelligent systems for many applications to the benefit of humanity. The book comes from a multidisciplinary subject whose audience can come from different academic departments, e.g., department of computer science and engineering, department of medical imaging, department of biomedical informatics, department of education sciences, and so on where artificial intelligence and soft computing are of routine courses. The book covers a range of audience from academicians, practitioners, researchers, and students to stakeholders. It can support graduate students and interns to develop a deep understanding of the latest paradigms in the soft computing techniques.

Artificial Intelligence and Soft Computing

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Publisher : Springer
ISBN 13 : 3319193694
Total Pages : 831 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Artificial Intelligence and Soft Computing by : Leszek Rutkowski

Download or read book Artificial Intelligence and Soft Computing written by Leszek Rutkowski and published by Springer. This book was released on 2015-06-04 with total page 831 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNAI 9119 and LNAI 9120 constitutes the refereed proceedings of the 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2015, held in Zakopane, Poland in June 2015. The 142 revised full papers presented in the volumes, were carefully reviewed and selected from 322 submissions. These proceedings present both traditional artificial intelligence methods and soft computing techniques. The goal is to bring together scientists representing both areas of research. The first volume covers topics as follows neural networks and their applications, fuzzy systems and their applications, evolutionary algorithms and their applications, classification and estimation, computer vision, image and speech analysis and the workshop: large-scale visual recognition and machine learning. The second volume has the focus on the following subjects: data mining, bioinformatics, biometrics and medical applications, concurrent and parallel processing, agent systems, robotics and control, artificial intelligence in modeling and simulation and various problems of artificial intelligence.

Machine Learning with PyTorch and Scikit-Learn

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Publisher : Packt Publishing Ltd
ISBN 13 : 1801816387
Total Pages : 775 pages
Book Rating : 4.8/5 (18 download)

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Book Synopsis Machine Learning with PyTorch and Scikit-Learn by : Sebastian Raschka

Download or read book Machine Learning with PyTorch and Scikit-Learn written by Sebastian Raschka and published by Packt Publishing Ltd. This book was released on 2022-02-25 with total page 775 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

Soft Computing: Theories and Applications

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Author :
Publisher : Springer Nature
ISBN 13 : 9811617406
Total Pages : 734 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Soft Computing: Theories and Applications by : Tarun K. Sharma

Download or read book Soft Computing: Theories and Applications written by Tarun K. Sharma and published by Springer Nature. This book was released on 2021-07-30 with total page 734 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on soft computing and how it can be applied to solve real-world problems arising in various domains, ranging from medicine and healthcare, to supply chain management, image processing and cryptanalysis. It gathers high-quality papers presented at the International Conference on Soft Computing: Theories and Applications (SoCTA 2020), organized online. The book is divided into two volumes and offers valuable insights into soft computing for teachers and researchers alike; the book will inspire further research in this dynamic field.

Soft Computing and Optimization

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Publisher : Springer Nature
ISBN 13 : 9811964068
Total Pages : 362 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Soft Computing and Optimization by : Syeda Darakhshan Jabeen

Download or read book Soft Computing and Optimization written by Syeda Darakhshan Jabeen and published by Springer Nature. This book was released on 2023-03-14 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book collects papers presented at the Virtual International Conference on Soft Computing, Optimization Theory and Applications (SCOTA 2021), held at the Birla Institute of Technology, Mesra, Ranchi, India, from 26–27 March 2021. Topics discussed in the book are on fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations, and their application in areas such as intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction and optimization of complex problems. The book highlights research on: (a) hybrid intelligent systems based on soft computing, new concepts and algorithms based on fuzzy logic and their applications, (b) theory and practice of meta-heuristics in different areas of application, (c) applications of fuzzy logic, (d) neural networks and hybrid intelligent systems in medical applications, (e) neural networks and optimization and evolutionary algorithms and their different applications and (f) applications of fuzzy logic, neural networks and meta-heuristics in pattern recognition problems. Some papers contain applications background like approximate solution of fractional differential equations via fixed-point algorithms and applications to equilibrium problems and image deburring problems. The book will be of great use to students, researchers and scientists in computer science, optimization and engineering, and those interested on computational intelligence and soft computing and their applications.

Advances in Soft Computing

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Publisher : Springer Nature
ISBN 13 : 3030608840
Total Pages : 511 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Advances in Soft Computing by : Lourdes Martínez-Villaseñor

Download or read book Advances in Soft Computing written by Lourdes Martínez-Villaseñor and published by Springer Nature. This book was released on 2020-10-07 with total page 511 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNAI 12468 and 12469 constitutes the proceedings of the 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, held in Mexico City, Mexico, in October 2020. The total of 77 papers presented in these two volumes was carefully reviewed and selected from 186 submissions. The contributions are organized in topical as follows: Part I: machine and deep learning, evolutionary and metaheuristic algorithms, and soft computing. Part II: natural language processing, image processing and pattern recognition, and intelligent applications and robotics.

New Advances in Soft Computing in Civil Engineering

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Publisher : Springer Nature
ISBN 13 : 3031659767
Total Pages : 425 pages
Book Rating : 4.0/5 (316 download)

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Book Synopsis New Advances in Soft Computing in Civil Engineering by : Gebrail Bekdaş

Download or read book New Advances in Soft Computing in Civil Engineering written by Gebrail Bekdaş and published by Springer Nature. This book was released on with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: