The hottest Math Substack posts right now

And their main takeaways
Category
Top Technology Topics

How your computer differentiates between positive and negative numbers [Math Mondays]

Technology Made Simple • 59 implied HN points • 06 Sep 22
🔬 Science Math Computer Science Quantum Computing
  1. Understanding how computers differentiate between positive and negative numbers is crucial in programming.
  2. Computers use memory bits to store the sign of numbers and their values, applying similar techniques for storing decimals.
  3. Breaking down complex problems into simpler, solved parts is a common approach in problem solving across software engineering and mathematics.

[Solution]Problem 54:Multiply Strings[Google]

Technology Made Simple • 59 implied HN points • 02 Sep 22
🕹 Technology Coding Software Engineering Math Algorithms
  1. The problem discussed involves multiplying two non-negative integers represented as strings without using built-in libraries or converting them directly to integers. This approach is used to prevent overflow.
  2. Understanding the process of multiplication and breaking it down into individual steps is crucial in tackling problems like this. It helps in figuring out the next steps and finding a path forward.
  3. Consider unique approaches, such as examining how computers encode characters and utilizing this knowledge to work around constraints. Building a strong foundation in software engineering fundamentals is beneficial for problem-solving.

Linear Inequalities [Math Mondays]

Technology Made Simple • 59 implied HN points • 30 Aug 22
🚌 Education Math Coding Learning
  1. Linear Inequalities are crucial in math for coding, although they may seem trivial at first.
  2. Linear Inequalities help codify bounds and constraints efficiently, aiding in filtering out bad solutions and narrowing search spaces.
  3. Practicing breaking down real-life situations into equations and inequalities is key to mastering Linear Inequalities.
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(My) shallow thoughts about deep learning in math, prelude

Silicon Reckoner • 19 implied HN points • 21 Jun 23
🔬 Science Math Technology Society Artificial Intelligence Ethics
  1. The workshop discussed deep learning in math, but overlooked larger implications like wealth and power distribution.
  2. The binary logic of 0s and 1s in computer science can be viewed as modern Manichaeism.
  3. AI can change audio playback speed using deep learning, offering quality results even with extreme speed changes.

Lessons from the Approximations to the Travelling Salesman Problem[Math Mondays]

Technology Made Simple • 39 implied HN points • 20 Sep 22
🕹 Technology Math Computer Science Coding Heuristics
  1. A good solution that can be deployed quickly is often better than an extremely complex optimal solution.
  2. Introducing mutations and heuristics can be beneficial in finding solutions, especially when working on heuristics instead of provably optimal solutions.
  3. Combining ideas from multiple domains can lead to innovative and valuable solutions in computer science.

[Solution]Problem 79: Checking for a value[Facebook/Meta]

Technology Made Simple • 19 implied HN points • 17 Mar 23
🕹 Technology Math Problem Solving
  1. The post discusses a coding problem involving mathematical or bit operations with 32-bit integers for conditional value return.
  2. The solution to the problem is highlighted as being deceptively simple in terms of code but with a significant impact, serving as an introduction to a common technique in computational problem-solving.
  3. The problem requires returning one of the integers based on the value of a third integer, and only allows the use of mathematical or bit operations while assuming the third integer can only be 1 or 0.

Math, Where are the static types?

Ron Friedhaber • 3 HN points • 26 May 24
🔬 Science Math Optimization Python
  1. Math notation focuses on simplification, not optimization, unlike in computer programming where efficiency is crucial.
  2. In math, statements are mostly immutable and remain so until proven true, contrasting with programs that are mutable to accommodate bugs and user requests.
  3. Python initially succeeded with dynamic typing for prototyping but has gradually shifted towards typed Python, reflecting a broader trend in the language's evolution.

Problem 78: Finding the Distance between 2 sets[Goldman Sachs]

Technology Made Simple • 19 implied HN points • 09 Mar 23
🕹 Technology Math Problem Solving Logic Generation
  1. The problem discusses finding the distance between two sets in math and problem-solving contexts, with connections to logic and generation.
  2. Terms like R^n and |x| are explained, providing insight into real number n-dimensional spaces and calculating distances.
  3. The post encourages solving rare interview-style questions, useful for senior positions or competitive programs, showcasing the value of handling diverse problem scenarios.

A fun no-calculator Problem [Math Monday]

Technology Made Simple • 39 implied HN points • 23 Aug 22
🚌 Education Math Learning Tutoring Problem Solving Interview Preparation
  1. The newsletter shared a fun math problem that doesn't require advanced knowledge, great for problem-solving practice.
  2. The author offered a limited-time 50% discount to celebrate almost 50 questions completed in the newsletter.
  3. There was a special request for readers to fill out a form to recommend the newsletter to Substack for more visibility.

Using Visualization to create better solutions [Technique Tuesdays]

Technology Made Simple • 39 implied HN points • 16 Aug 22
🕹 Technology Visualization Math Computer Science Algorithms Learning
  1. Visualization helps in learning and problem-solving by making connections and identifying patterns.
  2. When visualizing complex ideas, start small by breaking down components and building up from there.
  3. Developing visualization skills requires a strong understanding of the concepts and practicing visualization techniques regularly.

The art of making Simplifying Assumptions[Technique Tuesdays]

Technology Made Simple • 39 implied HN points • 26 Jul 22
🕹 Technology Problem Solving Math Programming Artificial Intelligence Software Engineering
  1. The technique of making simplifying assumptions can help in problem-solving in various areas like System Design, Competitive Programming, and Math.
  2. Loosening computational constraints and accepting tradeoffs in performance metrics can lead to more optimal solutions by focusing on key aspects rather than trying to solve everything at once.
  3. Reducing scale, simplifying input, and picking optimal battlegrounds are strategies to make problem-solving more manageable and effective by breaking down complex problems into simpler components.

Ramanujan, The Langlands Project, and Fermat's Last Theorem [Math Mondays]

Technology Made Simple • 39 implied HN points • 25 Jul 22
🚌 Education Math Research Learning
  1. The Langlands Project seeks to connect Geometry with Algebra, which is a significant development in mathematics.
  2. Connecting Geometry with Algebra has helped solve important problems like Fermat's Last Theorem, leading to substantial recognition and rewards.
  3. Even if you're not into math research, being aware of projects like Langlands can impact various aspects of the world and help you benefit from advancements in the field.

How seemingly 'unrelated' concepts can be helpful when solving complex problems [Math Monday]

Technology Made Simple • 39 implied HN points • 04 Jul 22
🚌 Education Math Problem Solving Coding Learning
  1. The solution to complex problems often combines ideas from multiple domains, similar to how problem-solving in software engineering works.
  2. When faced with intimidating problems, remember that breaking them down into smaller components and using various techniques is key to finding the solution.
  3. Techniques used in math and coding interviews are often similar, such as using small test cases, analyzing functions, and reducing problems to previously solved cases.

Using Bits as a way to Encode Preference[Technique Tuesdays]

Technology Made Simple • 39 implied HN points • 01 Jun 22
🕹 Technology Coding Data Structures Algorithms Math
  1. Using bit fields can significantly reduce storage requirements for tracking user preferences. A technique by Vimeo engineers allowed O(1) space compared to the traditional O(k) method, making the solution far more efficient.
  2. Bit fields utilize bitwise operators to represent content filters, enabling quick comparisons in constant time. This approach is memory and time-efficient.
  3. Implementing bit fields for tracking user preferences allows for efficient filtering of content by performing a bitwise AND operation between the user's and video's bit fields. This results in a quick eligibility check for the user.

[Solution]Problem 38: Power Function[Stripe]

Technology Made Simple • 39 implied HN points • 20 May 22
🕹 Technology Math Logic Optimization Recursion
  1. The problem focuses on implementing a power function without using built-in functions, showcasing the importance of base mathematical operations.
  2. Starting with a simple brute-force solution can lead to more efficient solutions and impress interviewers by demonstrating a structured problem-solving approach.
  3. Optimizations can be made by leveraging mathematics to improve the linear time complexity of the solution.

Problem 38: Power Function[Stripe]

Technology Made Simple • 39 implied HN points • 19 May 22
🕹 Technology Math Logic Optimization Recursion
  1. The post discusses implementing a power function that calculates x raised to the power n without using built-in functions, focusing on math, logic, optimization, and recursion.
  2. Examples of the power function implementation are provided with input-output pairs to demonstrate how it should work.
  3. There is a special request for feedback and sharing of topics for future focus, along with encouragement to explore additional content and subscribe for further tips and assistance.

Recurrence Relations[Math Mondays]

Technology Made Simple • 39 implied HN points • 10 May 22
🚌 Education Math Coding Algorithms Tutorial
  1. Recurrence relations are crucial in Computer Science and Discrete Math, defining the basis of Time Complexity.
  2. Understanding recurrence relations makes it easier to analyze code efficiency and computational requirements.
  3. By practicing common functions like sorting, searching, and analyzing their recurrence behavior, you can easily compute time complexity.

Problem 69: Generate Random Numbers According to Probability [TripleByte]

Technology Made Simple • 19 implied HN points • 15 Dec 22
🕹 Technology Coding Problem Solving Math
  1. The problem discussed involves generating random numbers based on given probabilities. For instance, if given numbers are [1, 2, 3, 4] and corresponding probabilities are [0.1, 0.5, 0.2, 0.2], the function should return 1 10% of the time, 2 50% of the time, and 3 and 4 20% of the time.
  2. The post includes information on job opportunities like a remote Machine Learning Engineer position and features a free weekly summary of AI and Machine Learning updates. There's also a call for sponsors for the newsletter.
  3. The textual content suggests subscribing to a resourceful newsletter for problem-solving tips and techniques in areas like Logic, Problem Solving, and Math. It also shares links to platforms where the author's content is available, such as YouTube and LinkedIn.

Idea behind Bit shifting [Math Monday]

Technology Made Simple • 39 implied HN points • 19 Apr 22
🕹 Technology Math Algorithms Computer Science
  1. Understanding Binary Math is crucial for coding interviews. Practice is key for mastering bit shifting.
  2. Familiarity with Modular Arithmetic, Number Systems, and Recursion is important. They are foundational math skills for solving interview questions.
  3. Being able to identify when to use Mod function, transitioning between number bases, and coding recursion are critical for successful problem-solving.

Recursion- A mathematical perspective [Math Monday]

Technology Made Simple • 39 implied HN points • 12 Apr 22
🚌 Education Math Recursion Proofs Computer Science Problem Solving
  1. Mathematical Induction is a technique for proving statements by starting with a base case and progressing through inductive steps. It forms the foundation for recursion.
  2. Both Mathematical Induction and recursion rely on base cases, operate on discrete domains, and reduce problems to already proven statements. They are like mirror images of each other in problem-solving.
  3. Understanding Mathematical Induction can greatly improve recursion skills as they share similar problem-solving approaches. Practicing PMI questions can enhance recursion proficiency.

[Solution]Problem 32: Maximize Average Pass Ratio[Goldman Sachs]

Technology Made Simple • 39 implied HN points • 08 Apr 22
🚌 Education Math Problem Solving Lists
  1. The problem revolves around maximizing the average pass ratio in multiple classes by strategically assigning extra students
  2. The key is to calculate the gain from adding a single extra student to each class and then determine which class benefits the most
  3. Breaking down complex problems into specific well-defined subproblems can make them more manageable and easier to solve

New Newsletter Structure

Technology Made Simple • 39 implied HN points • 13 Mar 22
🚌 Education Interview Preparation Math Techniques Finance System Design
  1. The newsletter is now going daily, offering a comprehensive schedule for coding interview preparation.
  2. The schedule includes themed days like Math Monday and Technique Tuesday, covering important aspects of coding interviews.
  3. Premium subscribers gain access to in-depth solutions, stories, and system design discussions, enhancing their interview preparation.

Problem 63: Find all occurrences of Substring [Microsoft]

Technology Made Simple • 19 implied HN points • 03 Nov 22
🕹 Technology Strings Hashing Math
  1. The post discusses finding all occurrences of a substring in a given string, emphasizing the need to identify starting indices.
  2. Devansh, the author, shares a personalized message and requests feedback from the readers by asking about their university experiences.
  3. Subscribers are encouraged to participate in a survey, fill out forms to support the newsletter, and explore additional content on different platforms of the author.

[Solution]Problem 51:Happy Number[IBM]

Technology Made Simple • 19 implied HN points • 11 Aug 22
🕹 Technology Coding Problem Solving Math Algorithm Software Engineering
  1. A happy number is a number defined by a specific process that ends with the number 1, while an unhappy number will loop endlessly without reaching 1.
  2. When facing a problem, break down the definitions given in the problem as this can provide insights and help formulate mathematical rules for quick problem-solving.
  3. In problem-solving, looking for patterns, mathematical or algorithmic statements can give a competitive advantage and aid in solving or optimizing problems efficiently.

[Solution]Problem 45:Robot Bounded In Circle [Amazon]

Technology Made Simple • 19 implied HN points • 07 Jul 22
🕹 Technology Math Logic Functions Problem Solving Coding
  1. Math and logic are essential in problem-solving as they can help analyze and solve complex scenarios.
  2. Understanding periodicity in functions can be applied to analyze repetitive patterns in problems with infinite instructions.
  3. Analyzing the impact of direction and location in a periodic function can reveal insights on the behavior of a system over time, leading to accurate solutions.

How to Spot Greedy Algorithms[Techinque Tuesdays]

Technology Made Simple • 19 implied HN points • 22 Jun 22
🕹 Technology Algorithms Coding Math AI Interviews
  1. Greedy algorithms make decisions based on immediate benefit, without looking ahead to future consequences.
  2. To identify a greedy algorithm, look for problems that can be broken into sub-problems, where the optimal solution of each sub-problem contributes to the overall optimal solution.
  3. Improving at recognizing greedy algorithms requires practice and analysis of problems to refine your intuition.

An intro to Functions [Math Mondays]

Technology Made Simple • 19 implied HN points • 21 Jun 22
🚌 Education Math Programming Computer Science Problem Solving Learning
  1. Understanding functions in math helps in becoming a better programmer by teaching how to frame problems as inputs and equations.
  2. Mastering math functions translates well to coding, as both domains involve transformations on inputs to get desired outputs.
  3. To get better at functions, focus on topics like Linear Programming and Precalculus, which can sharpen problem-solving and understanding of transformations.