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Author: Zoran Gacovski
Publisher: Arcler Press
ISBN: 9781773615004
Category : Computers
Languages : en
Pages : 0
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Book Description
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.
Author: Zoran Gacovski
Publisher: Arcler Press
ISBN: 9781773615004
Category : Computers
Languages : en
Pages : 0
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Book Description
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.
Author: Gypsy Nandi
Publisher: John Wiley & Sons
ISBN: 1394173156
Category : Computers
Languages : en
Pages : 356
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Book Description
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.
Author: CLAUS. FUHRER
Publisher:
ISBN: 9781838822323
Category :
Languages : en
Pages : 392
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Book Description
Leverage this example-packed, comprehensive guide for all your Python computational needs Key Features: Learn the first steps within Python to highly specialized concepts Explore examples and code snippets taken from typical programming situations within scientific computing. Delve into essential computer science concepts like iterating, object-oriented programming, testing, and MPI presented in strong connection to applications within scientific computing. Book Description: Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python. This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations. By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing. What You Will Learn: Understand the building blocks of computational mathematics, linear algebra, and related Python objects Use Matplotlib to create high-quality figures and graphics to draw and visualize results Apply object-oriented programming (OOP) to scientific computing in Python Discover how to use pandas to enter the world of data processing Handle exceptions for writing reliable and usable code Cover manual and automatic aspects of testing for scientific programming Get to grips with parallel computing to increase computation speed Who this book is for: This book is for students with a mathematical background, university teachers designing modern courses in programming, data scientists, researchers, developers, and anyone who wants to perform scientific computation in Python.
Author: Lisa Tagliaferri
Publisher: DigitalOcean
ISBN: 099977302X
Category : Computers
Languages : en
Pages : 146
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Book Description
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.
Author: Francois Chollet
Publisher: Simon and Schuster
ISBN: 1638352046
Category : Computers
Languages : en
Pages : 597
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Book Description
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
Author: Himanshu Singh
Publisher: Apress
ISBN: 1484253612
Category : Computers
Languages : en
Pages : 270
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Book Description
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.
Author: Sankar Kumar Pal
Publisher: Springer Science & Business Media
ISBN: 3642188591
Category : Computers
Languages : en
Pages : 741
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Book Description
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.
Author: Mehryar Mohri
Publisher: MIT Press
ISBN: 0262351366
Category : Computers
Languages : en
Pages : 505
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Book Description
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.
Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 1107057132
Category : Computers
Languages : en
Pages : 415
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Book Description
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author: S. RAJASEKARAN
Publisher: PHI Learning Pvt. Ltd.
ISBN: 8120321863
Category : Computers
Languages : en
Pages : 459
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Book Description
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.
