Deep Neuro Fuzzy Systems With Python

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Deep Neuro-Fuzzy Systems with Python

Author : Himanshu Singh
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.

Machine Learning Guide for Oil and Gas Using Python

Author : Hoss Belyadi
Publisher : Gulf Professional Publishing
ISBN 13 : 0128219300
Total Pages : 478 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Machine Learning Guide for Oil and Gas Using Python by : Hoss Belyadi

Download or read book Machine Learning Guide for Oil and Gas Using Python written by Hoss Belyadi and published by Gulf Professional Publishing. This book was released on 2021-04-09 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas Using Python details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges. - Helps readers understand how open-source Python can be utilized in practical oil and gas challenges - Covers the most commonly used algorithms for both supervised and unsupervised learning - Presents a balanced approach of both theory and practicality while progressing from introductory to advanced analytical techniques

Introduction to Fuzzy Logic

Author : James K. Peckol
Publisher : John Wiley & Sons
ISBN 13 : 1119772613
Total Pages : 308 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Introduction to Fuzzy Logic by : James K. Peckol

Download or read book Introduction to Fuzzy Logic written by James K. Peckol and published by John Wiley & Sons. This book was released on 2021-08-02 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn more about the history, foundations, and applications of fuzzy logic in this comprehensive resource by an academic leader Introduction to Fuzzy Logic delivers a high-level but accessible introduction to the rapidly growing and evolving field of fuzzy logic and its applications. Distinguished engineer, academic, and author James K. Peckol covers a wide variety of practical topics, including the differences between crisp and fuzzy logic, the people and professions who find fuzzy logic useful, and the advantages of using fuzzy logic. While the book assumes a solid foundation in embedded systems, including basic logic design, and C/C++ programming, it is written in a practical and easy-to-read style that engages the reader and assists in learning and retention. The author includes introductions of threshold and perceptron logic to further enhance the applicability of the material contained within. After introducing readers to the topic with a brief description of the history and development of the field, Introduction to Fuzzy Logic goes on to discuss a wide variety of foundational and advanced topics, like: A review of Boolean algebra, including logic minimization with algebraic means and Karnaugh maps A discussion of crisp sets, including classic set membership, set theory and operations, and basic classical crisp set properties A discussion of fuzzy sets, including the foundations of fuzzy sets logic, set membership functions, and fuzzy set properties An analysis of fuzzy inference and approximate reasoning, along with the concepts of containment and entailment and relations between fuzzy subsets Perfect for mid-level and upper-level undergraduate and graduate students in electrical, mechanical, and computer engineering courses, Introduction to Fuzzy Logic covers topics included in many artificial intelligence, computational intelligence, and soft computing courses. Math students and professionals in a wide variety of fields will also significantly benefit from the material covered in this book.

Intelligent Systems

Author : Murilo C. Naldi
Publisher : Springer Nature
ISBN 13 : 3031453921
Total Pages : 498 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis Intelligent Systems by : Murilo C. Naldi

Download or read book Intelligent Systems written by Murilo C. Naldi and published by Springer Nature. This book was released on 2023-10-11 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNAI 14195, 14196, and 14197 constitutes the refereed proceedings of the 12th Brazilian Conference on Intelligent Systems, BRACIS 2023, which took place in Belo Horizonte, Brazil, in September 2023. The 90 full papers included in the proceedings were carefully reviewed and selected from 242 submissions. They have been organized in topical sections as follows: Part I: Best papers; resource allocation and planning; rules and feature extraction; AI and education; agent systems; explainability; AI models; Part II: Transformer applications; convolutional neural networks; deep learning applications; reinforcement learning and GAN; classification; machine learning analysis; Part III: Evolutionary algorithms; optimization strategies; computer vision; language and models; graph neural networks; pattern recognition; AI applications.

Deep Learning with Python

Author : Francois Chollet
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

Recent Advances in Computational Intelligence and Cyber Security

Author : Ashok Kumar Singh
Publisher : CRC Press
ISBN 13 : 104012786X
Total Pages : 400 pages
Book Rating : 4.0/5 (41 download)

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Book Synopsis Recent Advances in Computational Intelligence and Cyber Security by : Ashok Kumar Singh

Download or read book Recent Advances in Computational Intelligence and Cyber Security written by Ashok Kumar Singh and published by CRC Press. This book was released on 2024-07-08 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the ever-accelerating tapestry of our digital age, the symbiotic relationship between computational intelligence and cyber security has become the linchpin of progress. The relentless pace of technological evolution and the ceaseless emergence of cyber threats demand not only adaptation but also an exploration of the forefronts of innovation and defence. Recent Advances in Computational Intelligence and Cyber security is a testament to the exhilarating journey undertaken by researchers, practitioners, and visionaries in these pivotal fields. Within the confines of this book, we embark on a captivating exploration of the cutting-edge developments that define the current state of computational intelligence and the intricate dance with the ever-evolving landscape of cyber security.

Transfer, Diffusion and Adoption of Next-Generation Digital Technologies

Author : Sujeet K. Sharma
Publisher : Springer Nature
ISBN 13 : 3031501926
Total Pages : 455 pages
Book Rating : 4.0/5 (315 download)

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Book Synopsis Transfer, Diffusion and Adoption of Next-Generation Digital Technologies by : Sujeet K. Sharma

Download or read book Transfer, Diffusion and Adoption of Next-Generation Digital Technologies written by Sujeet K. Sharma and published by Springer Nature. This book was released on 2023-12-12 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the IFIP WG 8.6 International Working Conference on Transfer and Diffusion of IT, TDIT 2023, which took place in Nagpur, India, in December 2023. The 87 full papers and 23 short papers presented in these proceedings were carefully reviewed and selected from 209 submissions. The papers are organized in the following topical sections: Volume I: Digital technologies (artificial intelligence) adoption; digital platforms and applications; digital technologies in e-governance; metaverse and marketing. Volume II: Emerging technologies adoption; general IT adoption; healthcare IT adoption. Volume III: Industry 4.0; transfer, diffusion and adoption of next-generation digital technologies; diffusion and adoption of information technology.

Current Problems and Ways of Industry Development: Equipment and Technologies

Author : Olga G. Shakirova
Publisher : Springer Nature
ISBN 13 : 3030694216
Total Pages : 1068 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Current Problems and Ways of Industry Development: Equipment and Technologies by : Olga G. Shakirova

Download or read book Current Problems and Ways of Industry Development: Equipment and Technologies written by Olga G. Shakirova and published by Springer Nature. This book was released on 2021-04-28 with total page 1068 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a reflection of the modern scientific view of current and future problems and prospects of industry development: equipment and technologies. It combines the results of advanced researches of industry development: equipment and technologies in the field of various sciences – both technical and humanitarian, the synthesis of which allowed forming a holistic meta-scientific concept of industry development: equipment and technologies. The book consists of two parts. The first part reflects technical problems and ways of industry development: equipment and technologies. It examines the promising technologies for modern industrial development, the technogenic factors of neo-industrialization in the context of digital economy, strategic guidelines for the industry development: equipment and technologies from the standpoint of sustainable development, as well as integration mechanisms for the industry development: equipment and technologies, and scientific support for their activation. In the second part, organizational and managerial problems and ways of industry development: equipment and technologies are disclosed. The industry development: equipment and technologies were studied: a view from the standpoint of economics and management, legal barriers to the industry development: equipment: and technologies and the prospects for overcoming them, the impact of globalization on the industry development: equipment: and technologies and recommendations for managing internationalization, as well as social issues of industry development: equipment and technologies in the aspect of human resource’s training and management. The book combines the best works presented at the International Research and Practice Conference" Actual Problems and Ways of Industry Development: Equipment and Technologies", organized by the Komsomolsk-on-Amur State University and the Institute of Scientific Communications and held in Komsomolsk-on-Amur (Russia) September 28–October 1, 2020. The target audience of the book is academic scientists studying issues of industry development: equipment and technologies, as well as industrial enterprises and government regulators of industry development: equipment and technologies.

Fuzzy Logic for Beginners

Author : Masao Mukaidono
Publisher : World Scientific
ISBN 13 : 9810245343
Total Pages : 117 pages
Book Rating : 4.8/5 (12 download)

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Book Synopsis Fuzzy Logic for Beginners by : Masao Mukaidono

Download or read book Fuzzy Logic for Beginners written by Masao Mukaidono and published by World Scientific. This book was released on 2001 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are many uncertainties in the real world. Fuzzy theory treats a kind of uncertainty called fuzziness, where it shows that the boundary of yes or no is ambiguous and appears in the meaning of words or is included in the subjunctives or recognition of human beings. Fuzzy theory is essential and is applicable to many systems -- from consumer products like washing machines or refrigerators to big systems like trains or subways. Recently, fuzzy theory has been a strong tool for combining new theories (called soft computing) such as genetic algorithms or neural networks to get knowledge from real data. This introductory book enables the reader to understand easily what fuzziness is and how one can apply fuzzy theory to real problems -- which explains why it was a best-seller in Japan.

Intelligent Projects Using Python

Author : Santanu Pattanayak
Publisher : Packt Publishing Ltd
ISBN 13 : 1788994868
Total Pages : 332 pages
Book Rating : 4.7/5 (889 download)

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Book Synopsis Intelligent Projects Using Python by : Santanu Pattanayak

Download or read book Intelligent Projects Using Python written by Santanu Pattanayak and published by Packt Publishing Ltd. This book was released on 2019-01-31 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Implement machine learning and deep learning methodologies to build smart, cognitive AI projects using Python Key FeaturesA go-to guide to help you master AI algorithms and concepts8 real-world projects tackling different challenges in healthcare, e-commerce, and surveillanceUse TensorFlow, Keras, and other Python libraries to implement smart AI applicationsBook Description This book will be a perfect companion if you want to build insightful projects from leading AI domains using Python. The book covers detailed implementation of projects from all the core disciplines of AI. We start by covering the basics of how to create smart systems using machine learning and deep learning techniques. You will assimilate various neural network architectures such as CNN, RNN, LSTM, to solve critical new world challenges. You will learn to train a model to detect diabetic retinopathy conditions in the human eye and create an intelligent system for performing a video-to-text translation. You will use the transfer learning technique in the healthcare domain and implement style transfer using GANs. Later you will learn to build AI-based recommendation systems, a mobile app for sentiment analysis and a powerful chatbot for carrying customer services. You will implement AI techniques in the cybersecurity domain to generate Captchas. Later you will train and build autonomous vehicles to self-drive using reinforcement learning. You will be using libraries from the Python ecosystem such as TensorFlow, Keras and more to bring the core aspects of machine learning, deep learning, and AI. By the end of this book, you will be skilled to build your own smart models for tackling any kind of AI problems without any hassle. What you will learnBuild an intelligent machine translation system using seq-2-seq neural translation machinesCreate AI applications using GAN and deploy smart mobile apps using TensorFlowTranslate videos into text using CNN and RNNImplement smart AI Chatbots, and integrate and extend them in several domainsCreate smart reinforcement, learning-based applications using Q-LearningBreak and generate CAPTCHA using Deep Learning and Adversarial Learning Who this book is for This book is intended for data scientists, machine learning professionals, and deep learning practitioners who are ready to extend their knowledge and potential in AI. If you want to build real-life smart systems to play a crucial role in every complex domain, then this book is what you need. Knowledge of Python programming and a familiarity with basic machine learning and deep learning concepts are expected to help you get the most out of the book

Artificial Intelligence for Big Data

Author : Anand Deshpande
Publisher : Packt Publishing Ltd
ISBN 13 : 1788476018
Total Pages : 371 pages
Book Rating : 4.7/5 (884 download)

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Book Synopsis Artificial Intelligence for Big Data by : Anand Deshpande

Download or read book Artificial Intelligence for Big Data written by Anand Deshpande and published by Packt Publishing Ltd. This book was released on 2018-05-22 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build next-generation Artificial Intelligence systems with Java Key Features Implement AI techniques to build smart applications using Deeplearning4j Perform big data analytics to derive quality insights using Spark MLlib Create self-learning systems using neural networks, NLP, and reinforcement learning Book Description In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems. What you will learn Manage Artificial Intelligence techniques for big data with Java Build smart systems to analyze data for enhanced customer experience Learn to use Artificial Intelligence frameworks for big data Understand complex problems with algorithms and Neuro-Fuzzy systems Design stratagems to leverage data using Machine Learning process Apply Deep Learning techniques to prepare data for modeling Construct models that learn from data using open source tools Analyze big data problems using scalable Machine Learning algorithms Who this book is for This book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus.

Deep Neural Networks for Multimodal Imaging and Biomedical Applications

Author : Suresh, Annamalai
Publisher : IGI Global
ISBN 13 : 1799835928
Total Pages : 294 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Deep Neural Networks for Multimodal Imaging and Biomedical Applications by : Suresh, Annamalai

Download or read book Deep Neural Networks for Multimodal Imaging and Biomedical Applications written by Suresh, Annamalai and published by IGI Global. This book was released on 2020-06-26 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of healthcare is seeing a rapid expansion of technological advancement within current medical practices. The implementation of technologies including neural networks, multi-model imaging, genetic algorithms, and soft computing are assisting in predicting and identifying diseases, diagnosing cancer, and the examination of cells. Implementing these biomedical technologies remains a challenge for hospitals worldwide, creating a need for research on the specific applications of these computational techniques. Deep Neural Networks for Multimodal Imaging and Biomedical Applications provides research exploring the theoretical and practical aspects of emerging data computing methods and imaging techniques within healthcare and biomedicine. The publication provides a complete set of information in a single module starting from developing deep neural networks to predicting disease by employing multi-modal imaging. Featuring coverage on a broad range of topics such as prediction models, edge computing, and quantitative measurements, this book is ideally designed for researchers, academicians, physicians, IT consultants, medical software developers, practitioners, policymakers, scholars, and students seeking current research on biomedical advancements and developing computational methods in healthcare.

Essentials of Excel VBA, Python, and R

Author : John Lee
Publisher : Springer Nature
ISBN 13 : 3031142837
Total Pages : 521 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Essentials of Excel VBA, Python, and R by : John Lee

Download or read book Essentials of Excel VBA, Python, and R written by John Lee and published by Springer Nature. This book was released on 2023-03-23 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: This advanced textbook for business statistics teaches, statistical analyses and research methods utilizing business case studies and financial data with the applications of Excel VBA, Python and R. Each chapter engages the reader with sample data drawn from individual stocks, stock indices, options, and futures. Now in its second edition, it has been expanded into two volumes, each of which is devoted to specific parts of the business analytics curriculum. To reflect the current age of data science and machine learning, the used applications have been updated from Minitab and SAS to Python and R, so that readers will be better prepared for the current industry. This second volume is designed for advanced courses in financial derivatives, risk management, and machine learning and financial management. In this volume we extensively use Excel, Python, and R to analyze the above-mentioned topics. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits. Readers can look to the first volume for dedicated content on financial statistics, and portfolio analysis.

Deep Learning Essentials

Author : Anurag Bhardwaj
Publisher : Packt Publishing Ltd
ISBN 13 : 1785887777
Total Pages : 271 pages
Book Rating : 4.7/5 (858 download)

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Book Synopsis Deep Learning Essentials by : Anurag Bhardwaj

Download or read book Deep Learning Essentials written by Anurag Bhardwaj and published by Packt Publishing Ltd. This book was released on 2018-01-30 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to grips with the essentials of deep learning by leveraging the power of Python Key Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Book Description Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications. What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who this book is for Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python.

Fuzzy Sets, Fuzzy Logic, And Fuzzy Systems: Selected Papers By Lotfi A Zadeh

Author : George J Klir
Publisher : World Scientific
ISBN 13 : 9814499811
Total Pages : 842 pages
Book Rating : 4.8/5 (144 download)

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Book Synopsis Fuzzy Sets, Fuzzy Logic, And Fuzzy Systems: Selected Papers By Lotfi A Zadeh by : George J Klir

Download or read book Fuzzy Sets, Fuzzy Logic, And Fuzzy Systems: Selected Papers By Lotfi A Zadeh written by George J Klir and published by World Scientific. This book was released on 1996-05-30 with total page 842 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book consists of selected papers written by the founder of fuzzy set theory, Lotfi A Zadeh. Since Zadeh is not only the founder of this field, but has also been the principal contributor to its development over the last 30 years, the papers contain virtually all the major ideas in fuzzy set theory, fuzzy logic, and fuzzy systems in their historical context. Many of the ideas presented in the papers are still open to further development. The book is thus an important resource for anyone interested in the areas of fuzzy set theory, fuzzy logic, and fuzzy systems, as well as their applications. Moreover, the book is also intended to play a useful role in higher education, as a rich source of supplementary reading in relevant courses and seminars.The book contains a bibliography of all papers published by Zadeh in the period 1949-1995. It also contains an introduction that traces the development of Zadeh's ideas pertaining to fuzzy sets, fuzzy logic, and fuzzy systems via his papers. The ideas range from his 1965 seminal idea of the concept of a fuzzy set to ideas reflecting his current interest in computing with words — a computing in which linguistic expressions are used in place of numbers.Places in the papers, where each idea is presented can easily be found by the reader via the Subject Index.

Deep Learning for Robot Perception and Cognition

Author : Alexandros Iosifidis
Publisher : Academic Press
ISBN 13 : 0323885721
Total Pages : 638 pages
Book Rating : 4.3/5 (238 download)

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Book Synopsis Deep Learning for Robot Perception and Cognition by : Alexandros Iosifidis

Download or read book Deep Learning for Robot Perception and Cognition written by Alexandros Iosifidis and published by Academic Press. This book was released on 2022-02-04 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. - Presents deep learning principles and methodologies - Explains the principles of applying end-to-end learning in robotics applications - Presents how to design and train deep learning models - Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more - Uses robotic simulation environments for training deep learning models - Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis

Deep Learning With Python

Author : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 266 pages
Book Rating : 4./5 ( download)

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Book Synopsis Deep Learning With Python by : Jason Brownlee

Download or read book Deep Learning With Python written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2016-05-13 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this Ebook, learn exactly how to get started and apply deep learning to your own machine learning projects.