NishMath

A series of Medium articles offer accessible explanations of diverse mathematical concepts and their real-world applications. Topics covered include solving various types of equations, from basic algebraic problems to more advanced exponential equations relevant to data science. One article provides a step-by-step guide to understanding and solving equations, emphasizing the importance of this skill across numerous fields like finance and programming. Another article tackles the frequency illusion, also known as the Baader-Meinhof phenomenon, explaining the cognitive bias behind why we notice things more frequently after becoming newly aware of them.

Furthermore, the collection explores the significant relationship between mathematics and coding, illustrating how mathematical principles underpin fundamental concepts in computer science such as algorithms, data structures, and computational complexity. The articles also include practical applications, like using exponential equations in data science and demonstrating the use of linear regression in predictive analytics. A selection of math puzzles with answers is also provided, offering engaging challenges for readers to test and hone their problem-solving skills.


References :

• Maths on Medium: This Medium article provides a comprehensive guide to understanding and solving mathematical equations.
• matt-connors.medium.com: This Medium post explains how to understand and solve equations.
• rossiangela.medium.com: What Your Math Tutor Isn’t Telling You (A Comprehensive Exposé of Mathematical Deception)
• medium.com: An article about eigenvectors and eigenvalues for data science.
• Maths on Medium: An article on Medium about an AI tool called AI Math Master which aims to simplify solving mathematical problems.
• medium.com: AI Math Master: Your Ultimate Tool for Effortless Math Problem Solving
• medium.com: Article describing how to solve systems of linear equations using LU decomposition.
• Statistics on Medium: This Medium article discusses the integral of a normal distribution.
• Maths on Medium: This Medium post teaches math using C++ coding examples, focusing on arithmetic numbers.
• Statistics on Medium: An article about mastering mathematics for data science interviews.
• medium.com: Medium article on solving the equation 5^x + 25^x = 125^x.
• medium.com: An article providing a complete guide to descriptive statistics.
• medium.com: An article on statistics for data scientists.

Classification:

• HashTags: mathematics algebra coding
• Company: Medium
• Target: MathematicalSkills
• Product: AppliedMathematics
• Feature: MathConcepts
• Type: Tutorial
• Severity: Medium

This year's Nobel Prize in Physics has been awarded to John Hopfield of Princeton University and Geoffrey Hinton of the University of Toronto. The Royal Swedish Academy of Sciences recognized their "foundational discoveries and inventions that enable machine learning with artificial neural networks." Hopfield's research centered on associative memory using Hopfield networks, while Hinton's contributions focused on methods for autonomously identifying patterns within data, utilizing Boltzmann machines. Their work is considered groundbreaking in the field of artificial intelligence and has implications for various areas of physics, including the creation of novel materials.

The award has sparked debate within the physics community, with some questioning the appropriateness of awarding a Physics Nobel for work primarily in computer science. While Hopfield's background is in condensed matter physics and his work draws inspiration from concepts like spin glass theory, Hinton's background is in artificial intelligence. The choice reflects the increasing interconnectedness and influence of computer science on other scientific fields, pushing the boundaries of traditional disciplinary lines.

Despite the controversy, the Nobel committee has underscored the fundamental contributions of Hopfield and Hinton. Their innovative work on artificial neural networks, drawing upon and extending principles of statistical physics, has revolutionized machine learning, creating significant advancements with far-reaching applications beyond the realm of physics. The prize is a testament to the groundbreaking nature of their research and its transformative impact on multiple scientific and technological areas.


References :

• AMS News: The Royal Swedish Academy of Sciences has awarded the 2024 Nobel Prize in Physics to , Princeton University, and , University of Toronto, “for foundational discoveries and inventions that enable machine learning with artificial neural networks.
• 4 gravitons: The 2024 Physics Nobel Prize was announced this week, awarded to John Hopfield and Geoffrey Hinton for using physics to propose foundational ideas in the artificial neural networks used for machine learning.
• Albert Cardona: Nobel Prize to the Statistical Physics of artificial neural networks – why that's about physics and can't be otherwise.
• manlius.substack.com: Substack article explaining the Nobel Prize in Physics.
• Daniel Lemire's blog: Blog post about Geoffrey Hinton's Nobel Prize in Physics.
• mathstodon.xyz: Blog post by Manlio de Domenico on the Nobel Prize in Physics for contributions to artificial neural networks.
• Daniel Lemire's blog: This blog post discusses the 2024 Nobel Prize in Physics being awarded to computer scientists, reflecting on the increasing influence of computer science in natural sciences.
• tritonstation.com: This blog post discusses the 2024 Nobel Prize in Physics award to computer scientists and considers whether this suggests a lack of new ideas in Physics.

Classification:

• HashTags: NobelPrize Physics MachineLearning
• Company: PrincetonUniversity
• Target: MachineLearning
• Product: Physics
• Feature: NeuralNetworks
• Type: Research
• Severity: Major