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Topics /
AI & Machine Learning
The most popular blog posts about ai & machine learning on Hacker News. 109 posts.
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Implementing a Neural Network from Scratch in Python
A simple explanation of how they work and how to implement one from scratch in Python.
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Scott’s Supreme Quantum Supremacy FAQ
You’ve seen the stories—in the Financial Times, Technology Review, CNET, Facebook, Reddit, Twitter, or elsewhere—saying that a group at Google has now achieved quantum computation…
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A dumb reason computer vision apps aren’t working: Exif Orientation
I’ve written about lots of computer vision and machine learning projects like object recognition systems and face recognition projects. I…
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Visual Information Theory (2015)
I love the feeling of having a new way to think about the world. I especially love when there’s some vague idea that gets formalized into a concrete concept. Information theory is a prime example of this.
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Humans who are not concentrating are not general intelligences
Recently, OpenAI came out with a new language model that automatically synthesizes text, called GPT-2. It’s disturbingly good. You can see some examples (cherry-picked, by their own admissio…
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Random Forests for Complete Beginners
The definitive guide to Random Forests and Decision Trees.
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A Recipe for Training Neural Networks
Musings of a Computer Scientist.
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Remastering Star Trek: Deep Space Nine with Machine Learning
The site for the Remako Mod, a Final Fantasy VII mod that uses AI to improve the graphics of this classic JRPG.
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The Illustrated Word2vec
Discussions: Hacker News (347 points, 37 comments), Reddit r/MachineLearning (151 points, 19 comments) Translations: Chinese (Simplified), French, Korean, Portuguese, Russian “There is in all things a pattern that is part of our universe. It has symmetry, elegance, and grace - those qualities you find always in that…
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Weaponizing and Gamifying AI for WiFi Hacking: Presenting Pwnagotchi 1.0.0
This is the story of a summer project that started out of boredom and that evolved into something incredibly fun and unique. It is also the story of how that p…
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Successful machine learning models: lessons learned at Booking.com
150 successful machine learning models: 6 lessons learned at Booking.com Bernadi et al., KDD’19 Here’s a paper that will reward careful study for many organisations. We’ve previously looked a…
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YOLO: Real-Time Object Detection
You only look once (YOLO) is a state-of-the-art, real-time object detection system. On a Pascal Titan X it processes images at 30 FPS and has a mAP of 57.9% on COCO test-dev.
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A Gentle Introduction to Bayes’ Theorem for Machine Learning
Bayes Theorem provides a principled way for calculating a conditional probability. It is a deceptively simple calculation, although it can be used to easily calculate the conditional probability of events where intuition often fails. Although it is a powerful tool in the field of probability, Bayes Theorem is also…
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ML From Scratch, Part 1: Linear Regression
To kick off this series, will start with something simple yet foundational: linear regression via ordinary least squares. While not particularly exciting, linear regression finds widespread use both as a standalone learning algorithm and as a building block in more advanced learning algorithms.
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Python vs. Rust for Neural Networks
Comparing a simple neural network in Rust and Python
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Fun with Snapchat's Gender Swapping Filter
Snapchat's new gender-bending filter is a source of endless fun and laughs at parties. The results are very pleasing to look at. As someone ...
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An Introduction to Recurrent Neural Networks
A simple walkthrough of what RNNs are, how they work, and how to build one from scratch in Python.
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Quantum supremacy: the gloves are off
Links:Google paper in NatureNew York Times articleIBM paper and blog post responding to Google’s announcementBoaz Barak’s new post: “Boaz’s inferior classical inferiority FA…
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Transformers from Scratch
Repository for personal website
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Gaussian Processes, not quite for dummies
Before diving inFor a long time, I recall having this vague impression about Gaussian Processes (GPs) being able to magically define probability distributions over sets of functions, yet I procrastinated reading up about them for many many moons. However, as always, I’d like to think that this is not just due to my…
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Viewing Matrices and Probability as Graphs
Today I'd like to share an idea. It's a very simple idea. It's not fancy and it's certainly not new. In fact, I'm sure many of you have thought about it already. But if you haven't—and even if you have!—I hope you'll take a few minutes to enjoy it with me. Here's the idea: Every matrix corresponds to a graph. So…
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Implementing a Convolutional Neural Network from Scratch in Python
A simple guide to what CNNs are, how they work, and how to build one from scratch in Python.
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How I Eat for Free in NYC Using Python, Automation, AI, and Instagram
Living and working in the big apple comes with big rent.
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Matrices as Tensor Network Diagrams
In the previous post, I described a simple way to think about matrices, namely as bipartite graphs. Today I'd like to share a different way to picture matrices—one which is used not only in mathematics, but also in physics and machine learning. Here's the basic idea. An $m\times n$ matrix $M$ with real entries…