Soft Computing And Machine Learning With Python
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Soft Computing and Machine Learning with Python
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.
Soft Computing and Machine Learning
This reference text covers the theory and applications of soft computing and machine learning and presents readers with the intelligent fuzzy and neutrosophic rules that require situations where classical modeling approaches cannot be utilized, such as when there is incomplete, unclear, or imprecise information at hand or inadequate data. It further illustrates topics such as image processing, and power system analysis. This book: Discusses soft computing techniques including fuzzy Logic, rough sets, neutrosophic sets, neural networks, generative adversarial networks, and evolutionary computation Examines novel and contemporary advances in the fields of soft computing, fuzzy computing, neutrosophic computing, and machine learning systems, as well as their applications in real life Serves as a comprehensive reference for applying machine learning and neutrosophic sets in real-world applications such as smart cities, healthcare, and the Internet of Things Covers topics such as image processing, bioinformatics, natural language processing, supply chain management, and cybernetics Illustrates classification of neutrosophic machine learning, neutrosophic reinforcement learning, and applications of neutrosophic machine learning in emerging industries The text is written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.
Principles of Soft Computing Using Python Programming
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.