Algorithmic Probability: Fundamentals and Applications

Β· One Billion Knowledgeable Β· ΠžΠ·Π²ΡƒΡ‡Π΅Π½ΠΎ ИИ Mason (ΠΎΡ‚ Google)
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2Β Ρ‡. 37Β ΠΌΠΈΠ½.
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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 15Β 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 Algorithmic Probability


In the field of algorithmic information theory, algorithmic probability is a mathematical method that assigns a prior probability to a given observation. This method is sometimes referred to as Solomonoff probability. In the 1960s, Ray Solomonoff was the one who came up with the idea. It has applications in the theory of inductive reasoning as well as the analysis of algorithms. Solomonoff combines Bayes' rule and the technique in order to derive probabilities of prediction for an algorithm's future outputs. He does this within the context of his broad theory of inductive inference.


How You Will Benefit


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


Chapter 1: Algorithmic Probability


Chapter 2: Kolmogorov Complexity


Chapter 3: Gregory Chaitin


Chapter 4: Ray Solomonoff


Chapter 5: Solomonoff's Theory of Inductive Inference


Chapter 6: Algorithmic Information Theory


Chapter 7: Algorithmically Random Sequence


Chapter 8: Minimum Description Length


Chapter 9: Computational Learning Theory


Chapter 10: Inductive Probability


(II) Answering the public top questions about algorithmic probability.


(III) Real world examples for the usage of algorithmic probability in many fields.


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

Об Π°Π²Ρ‚ΠΎΡ€Π΅

Fouad Sabry is the former Regional Head of Business Development for Applications at HP in Southern Europe, Middle East, and Africa (SEMEA). Fouad has received his B.Sc. of Computer Systems and Automatic Control in 1996, dual masterҀ™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.Β 

Fouad has more than 20 years of experience in Information Technology and Telecommunications fields, working in local, regional, and international companies, such as Vodafone and IBM in Middle East and Africa (MEA) region. Fouad joined HP Middle East (ME), based in Dubai, United Arab Emirates (UAE) in 2013 and helped develop the software business in tens of markets across Southern Europe, Middle East, and Africa (SEMEA) regions. Currently, Fouad is an entrepreneur, author, futurist, focused on Emerging Technologies, and Industry Solutions, and founder of One Billion Knowledgeable (1BK) Initiative.

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

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