Neural Modeling Fields: Fundamentals and Applications

Β· Artificial Intelligence Книга 210 Β· One Billion Knowledgeable Β· ΠžΠ·Π²ΡƒΡ‡Π΅Π½ΠΎ ИИ Mason (ΠΎΡ‚ Google)
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4Β Ρ‡. 6Β ΠΌΠΈΠ½.
Полная вСрсия
МоТно Π΄ΠΎΠ±Π°Π²ΠΈΡ‚ΡŒ
ΠžΠ·Π²ΡƒΡ‡Π΅Π½ΠΎ ИИ
ΠžΡ†Π΅Π½ΠΊΠΈ ΠΈ ΠΎΡ‚Π·Ρ‹Π²Ρ‹ Π½Π΅ ΠΏΡ€ΠΎΠ²Π΅Ρ€Π΅Π½Ρ‹. ΠŸΠΎΠ΄Ρ€ΠΎΠ±Π½Π΅Π΅β€¦
Π”ΠΎΠ±Π°Π²ΠΈΡ‚ΡŒ ΠΎΡ‚Ρ€Ρ‹Π²ΠΎΠΊ Π΄Π»ΠΈΠ½ΠΎΠΉ 24Β ΠΌΠΈΠ½.? Π•Π³ΠΎ ΠΌΠΎΠΆΠ½ΠΎ ΡΠ»ΡƒΡˆΠ°Ρ‚ΡŒ Π² любоС врСмя, Π΄Π°ΠΆΠ΅ ΠΎΡ„Π»Π°ΠΉΠ½.Β 
Π”ΠΎΠ±Π°Π²ΠΈΡ‚ΡŒ

Об Π°ΡƒΠ΄ΠΈΠΎΠΊΠ½ΠΈΠ³Π΅

What Is Neural Modeling Fields


Neural modeling field (NMF) is a mathematical framework for machine learning that integrates ideas from neural networks, fuzzy logic, and model based recognition. Its acronym stands for "Neural Modeling Field." Modeling fields, modeling fields theory (MFT), and Maximum likelihood artificial neural networks (MLANS) are some of the other names that have been used to refer to this concept.At the AFRL, Leonid Perlovsky is the one responsible for developing this framework. The NMF can be understood as a mathematical description of the machinery that make up the mind. These mechanisms include ideas, feelings, instincts, imagination, reasoning, and comprehension. The NMF is organized in a hetero-hierarchical structure that contains many levels. There are concept-models that encapsulate the knowledge at each level of the NMF. These concept-models generate so-called top-down signals, which interact with input signals that come from lower levels. These interactions are governed by dynamic equations, which are responsible for driving concept-model learning, adaptation, and the development of new concept-models for better correspondence to the input, bottom-up signals.


How You Will Benefit


(I) Insights, and validations about the following topics:


Chapter 1: Neural modeling fields


Chapter 2: Machine learning


Chapter 3: Supervised learning


Chapter 4: Unsupervised learning


Chapter 5: Weak supervision


Chapter 6: Reinforcement learning


Chapter 7: Neural network


Chapter 8: Artificial neural network


Chapter 9: Fuzzy logic


Chapter 10: Adaptive neuro fuzzy inference system


(II) Answering the public top questions about neural modeling fields.


(III) Real world examples for the usage of neural modeling fields in many fields.


(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of neural modeling fields' technologies.


Who This Book Is For


Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of neural modeling fields.

Об Π°Π²Ρ‚ΠΎΡ€Π΅

Fouad Sabry is the former Regional Head of Business Development for Applications at HP. Fouad has received his B.Sc. of Computer Systems and Automatic Control in 1996, dual master’s degrees from University of Melbourne (UoM) in Australia, Master of Business Administration (MBA) in 2008, and Master of Management in Information Technology (MMIT) in 2010. Fouad has more than 30 years of experience in Information Technology and Telecommunications fields, working in local, regional, and international companies, such as Vodafone and IBM. Fouad joined HP in 2013 and helped develop the business in tens of markets. Currently, Fouad is an entrepreneur, author, futurist, and founder of One Billion Knowledge (1BK) Initiative.

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ΠŸΡ€ΠΎΠ΄ΠΎΠ»ΠΆΠ΅Π½ΠΈΠ΅ сСрии

Π”Ρ€ΡƒΠ³ΠΈΠ΅ ΠΊΠ½ΠΈΠ³ΠΈ Π°Π²Ρ‚ΠΎΡ€Π° Fouad Sabry

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