The Curious Engineer

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The Palindrome

thepalindrome.org

mathematics ∪ machine learning. Click to read The Palindrome, a Substack publication with tens of thousands of subscribers.

MathAI / MLPython

’22’23’24’25’26
Posts
157 from 1 Dec 2022 to 6 Oct 2026
Posting
weekly (49 in the last year)
Kind
personal blog
Feed
https://thepalindrome.org/feed

2026 35

  1. Graph Theory for Visual Learners
  2. Teaser: Graph Theory for Visual Learners
  3. You Asked for Graph Theory. I’m Going All In.
  4. The Single Most Undervalued Fact of Linear Algebra
  5. I Started an Epidemic in Liberty City. Can I Stop It With Math?
  6. An Exclusive Preview of My Next Video
  7. 1096 Pages of Mathematics and Machine Learning
  8. Why I'm Slowing Down
  9. This Week at The Palindrome
  10. Mommy Trained a Neural Network
  11. The Math You Missed Behind Gradient Descent
  12. Quick reader survey — 2 minutes
  13. I Hated Coding on Paper. It Made Me Smarter.
  14. How to OOP in Python Like a Pro
  15. Explore LLM word representations using similarity analysis (part 2)
  16. The 10 Most Important Lessons 20 Years of Mathematics Taught Me
  17. Introduction to Object-Oriented Programming in Python
  18. Explore LLM word representations using similarity analysis (part 1)
  19. I Built the Knowledge Graph of Machine Learning
  20. This Week at The Palindrome
  21. The Power of Mathematical Modeling
  22. Explore Machine Learning as a Knowledge Graph
  23. This Week at The Palindrome (2025, Week 13)
  24. Mathematics of Machine Learning workshop
  25. Machine Learning From Zero, Chapter 01
  26. Our Heuristics to Spot AI-Generated Content are Useless
  27. Let’s Bring Jupyter Notebooks to Substack
  28. Accio Insights: The Marauder’s Map of the ML World
  29. The Palindrome in 2026
  30. The Day My Project Went to Space
  31. Why is the Golden Ratio Hiding in the Fibonacci Sequence?
  32. How did the Babylonians know √2 up to six digits?
  33. I Finally Listened to You and I’m So Glad I Did
  34. The Camel Principle: Why Adding Zero is the Most Powerful Trick in Mathematics
  35. The Single Most Undervalued Fact of Linear Algebra

2025 65

  1. Kids Are Tiny Scientists When It Comes to Debunking Santa
  2. The 10 Most Popular Posts of The Palindrome in 2025
  3. Visualizing the Chain Rule
  4. If You Want to Hit Your Target, Take More Shots
  5. The Fascinating Story of the Exponential Function
  6. The Math Behind Your Commute
  7. The Story of the Mathematics of Machine Learning Book
  8. Help Shape the Future of The Palindrome
  9. Derivatives in One, Two, and a Billion Variables
  10. The Art of Stupid
  11. The Anatomy of the Least Squares Method, Part Four
  12. The Anatomy of the Least Squares Method, Part Three
  13. October recap
  14. Coding on Paper
  15. The Palindrome Office Hours
  16. Unscripted with Mike X Cohen
  17. Unscripted with Miguel Otero Pedrido
  18. Computational Graphs in Python
  19. The Palindrome live schedule for October
  20. The Anatomy of the Least Squares Method, Part Two
  21. The Palindrome Office Hours - first official live stream!
  22. The Anatomy of the Least Squares Method, Part One
  23. Introduction to Computational Graphs
  24. How I Make My Illustrations
  25. How to Measure Information
  26. Vectors and Matrices
  27. Graphs and Boards
  28. Machine Learning Is Not Just Statistics
  29. Making Neural Networks Cheaper, Smaller, and Faster
  30. Representing Graphs
  31. Comparing Algorithms
  32. Correlation vs. cosine similarity
  33. The Competitive Programmer's Introduction to Graph Theory
  34. August recap + what's coming in September
  35. Matrices and graphs
  36. Introduction to Algorithmic Analysis
  37. Announcing The Palindrome lecture series
  38. Education or Experience? Both.
  39. What is infinity and how many of them are there?
  40. Your Machine Learning Library
  41. The Roadmap of Mathematics for Machine Learning
  42. The Palindrome is Growing
  43. The Science of Science
  44. What Elden Ring Has Taught Me About Hard Things
  45. Forward vs Backward Differentiation
  46. Do You Have to Learn Math for Machine Learning?
  47. There Are Many Roads to Machine Learning
  48. Your Brain is Wrong About Probability
  49. You get to decide the topic of the upcoming course
  50. The 10 Most Important Lessons 20 Years of Mathematics Taught Me
  51. The Most Important Skill You Were Never Taught
  52. What Full Body Composition Scales Teach About Machine Learning
  53. The Obstacle is the Path
  54. The Anatomy of Logistic Regression
  55. The Derivatives of Computational Graphs
  56. Computational Graphs and the Forward Pass
  57. The official preview of Mathematics of Machine Learning is here!
  58. Computational Graphs as Neural Networks
  59. Visualizing mathematics with Nicolas Garcia Belmonte
  60. Introduction to Computational Graphs
  61. The Camel Principle
  62. Update: "Can Large Language Models replace mathematicians?" fixed!
  63. Can Large Language Models replace mathematicians?
  64. Making AI cheaper, smaller, and faster
  65. Mathematics of Machine Learning is Out Now!

2024 18

  1. How to implement a decision tree
  2. How to grow a decision tree
  3. Machine Learning From Zero is ready to go!
  4. Machine Learning From Zero
  5. Epsilons, no. 7: What is a kernel?
  6. Vectorization in practice
  7. Understanding k-Nearest Neighbors
  8. A reflection on The Palindrome's journey upon passing 15k subscribers
  9. Vectorization in theory
  10. The Computer Scientist's Guide to Graph Theory, ep. 04
  11. Linear Regression and The Exploding Gradients
  12. Min-Max Math, ep. 07: Sets, relations, and functions
  13. Linear regression in multiple variables
  14. The mathematics of optimization for deep learning
  15. Building an interface for machine learning
  16. Logistic regression
  17. The Computer Scientist's Guide to Graph Theory, ep. 03
  18. The Maximum Likelihood Estimation

2023 34

  1. Where does the mean squared error come from?
  2. The Computer Scientist's Guide to Graph Theory, ep. 02
  3. Linear regression and the least squares problem
  4. Linear regression (is the most important algorithm ever)
  5. Birthday Special Offer
  6. The Computer Scientist's Guide to Graph Theory, ep. 01
  7. The Computer Scientist's Guide to Graph Theory, ep. 00
  8. A conversation with Alejandro Piad Morffis about education
  9. The taxonomy of machine learning paradigms
  10. Why are neural networks so powerful?
  11. How large that number in the Law of Large Numbers is?
  12. Min-Max Math, ep. 06: Proof by induction and contradiction
  13. Is probability frequentist or Bayesian?
  14. Machine learning is not just statistics
  15. How does the Japanese multiplication work?
  16. Min-Max Math, ep. 05: Proofs are (not) magic
  17. Epsilons, no. 6: What is that Σ?
  18. Min-Max Math, ep. 04: The (art and) science of reasoning
  19. Min-Max Math, ep. 03: At the roots of mathematics
  20. Min-Max Math, ep. 02: Definitions, theorems, proofs
  21. Min-Max Math, ep. 01: What is mathematics?
  22. How to add infinitely many numbers together?
  23. Epsilons, no. 5: The QR decomposition
  24. Epsilons, no. 4: The Gram-Schmidt process
  25. Why does gradient descent work?
  26. Epsilons, no. 3: The LU decomposition
  27. Epsilons, no. 2: Understanding matrix multiplication
  28. Epsilons, no. 1: The geometric series
  29. 1111111111 is a random sequence
  30. The Law of Large Numbers
  31. The fascinating story of the exponential function
  32. The non-recursive formula for Fibonacci numbers
  33. What's the meaning of the expected value?
  34. Probabilities, densities, and distributions

2022 5

  1. How did the Babylonians know √2 up to six digits?
  2. The history of trigonometric functions
  3. The unreasonable effectiveness of orthogonal systems
  4. How to measure the angle between two functions
  5. The science of science