Lecture19
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This document provides an overview of genetic fuzzy systems. It begins with a recap of supervised and unsupervised machine learning techniques. It then discusses fuzzy logics and how they can be used to represent imprecise concepts using membership functions. Fuzzy systems that use fuzzy logic to model relationships between variables are introduced. Genetic fuzzy systems combine fuzzy systems with genetic algorithms to design the fuzzy rules, membership functions, and inference engines. The genetic algorithm evolves populations of candidate fuzzy systems through selection, crossover and mutation to optimize system performance.
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[DSC Europe 23] Danijela Horak - The Innovator’s Dilemma: to Build or Not to ...DataScienceConferenc1 In this talk, we will delve into the evolving landscape of firms with established in-house machine learning teams and explore the critical question of their future role in the ever-changing world of AI. As the remit of these teams undergoes a seismic shift, a fundamental dilemma arises: Should these teams transition into AI engineering roles, leveraging commercially available tools and adapting their strategies around them, or should they continue to pioneer innovations within their specialised domains? This dilemma is unique to organizations with existing ML capabilities, setting them apart from firms without such teams. Furthermore, the talk will address the intricacies of using retrieval-augmented generation (RAG) as a potential solution in knowledge-intensive tasks, and ask whether such a method is a "hack" or a grounded methodology that will withstand the test of time. We will also reflect on the safe and responsible development of LLM-powered tools.
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2) Neural networks, including single layer networks, multilayer perceptrons, unsupervised learning networks
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The document discusses key concepts in soft computing like fuzzy logic, neural networks, evolutionary algorithms and their applications in areas like control systems and pattern recognition. It also provides references for further reading.
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1. Deep learning involves learning multiple levels of representations or features from raw input in a hierarchical manner, unlike traditional machine learning which uses engineered features.
2. Learning hierarchical representations is important because natural data lies on low-dimensional manifolds and disentangling the factors of variation can lead to more robust features.
3. Architectures for deep learning involve multiple levels of non-linear feature transformations followed by pooling to build increasingly abstract representations at each level. This allows the representations to become more invariant and disentangled.
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Pc Seminar Jordi
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Introduction to soft computing
Introduction to soft computingSiksha 'O' Anusandhan (Deemed to be University ) Present introductory level to soft computing.
Briefly present about neural network, fuzzy logic and evolutionary optimization.
Artificial intelligence quiz ai and fuzzy logic priti sajja
Artificial intelligence quiz ai and fuzzy logic priti sajjaPriti Srinivas Sajja Priti Srinivas Sajja is an Associate Professor working with Post Graduate Department of Computer Science, Sardar Patel University, India since 1994. She specializes in Artificial Intelligence especially in knowledge-based systems, soft computing and multiagent systems. She is co-author of Knowledge-Based Systems (2009) and Intelligent Technologies for Web Applications (2012). She is Principal Investigator of a major research project funded by UGC, India.
She has 113 publications in books, book chapters, journals, and in the proceedings of national and international conferences. Her four publications have won best research paper awards. for more detail, please visir pritisajja.info.
Gerald.mulenburg
Gerald.mulenburgNASAPMC This document summarizes a presentation on managing complex projects given by Dr. Jerry Mulenburg. It discusses complexity and the need for project managers of complex projects to have certain abilities. Complex projects are characterized by many interconnected parts, ambiguity, uncertainty, and rapid change. Managing complex projects requires understanding both tangible and intangible aspects. It also requires the capacity for parallel processing of information, drawing from experience, intelligence, and the ability to deal with unknown unknowns.
Basics of Soft Computing 
Basics of Soft Computing Sangeetha Rajesh Soft computing is an approach to computing that aims to model human-like decision making. It deals with imprecise or uncertain data using techniques like fuzzy logic, neural networks, and genetic algorithms. The goal is to develop systems that are tolerant of imprecision, uncertainty, and approximation to achieve practical and low-cost solutions to real-world problems. Soft computing was initiated in 1981 and includes fields like fuzzy logic, neural networks, and evolutionary computation. It provides approximate solutions using techniques like neural network reasoning, genetic programming, and functional approximation.
Fuzzy logic
Fuzzy logicSumeraHangi Fuzzy logic is a form of knowledge representation that allows for notions without precise definitions. It was introduced in 1965 by Lotfi Zadeh as an extension of traditional binary sets to fuzzy sets where elements can belong to multiple sets with varying degrees of membership. Fuzzy logic uses fuzzy sets that assign membership degrees between 0 and 1 rather than crisp 0 or 1 values, allowing for flexible and easy implementation of machine learning techniques to mimic human reasoning. It has been applied in various consumer products and industrial systems such as anti-lock brakes, auto transmissions, copy machines, and cruise control.
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