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Topics /
AI & Machine Learning
The most popular blog posts about ai & machine learning on Hacker News. 101 posts.
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Illustrated FixMatch for semi-supervised learning
Learn how to leverage unlabeled data using FixMatch for semi-supervised learning
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GPT-3 Creative Fiction
Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.
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Which GPUs to get for deep learning
Here, I provide an in-depth analysis of GPUs for deep learning/machine learning and explain what is the best GPU for your use-case and budget.
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Why GPT-3 Matters
The sheer scale of the new GPT-3 model is hard to overstate; it’s an entire order of magnitude larger than Microsoft’s already-massive 17B parameter Turing-NLG.[1] Loading the entire model’s weights
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Quantum supremacy, now with BosonSampling
Update (12/5): The Google team, along with Gil Kalai, have raised questions about whether the results of the new BosonSampling experiment might be easier to spoof classically than the USTC team tho…
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Visual Introduction to Self Supervised Learning
A visual introduction to self-supervised learning methods for visual representations.
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How much did AlphaGo Zero cost? (2018)
Answer: probably around $35 million.
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Score matching with Langevin Sampling: a new contender to GANs
Last year, Yang Song, a graduate student at Stanford, showed an entirely new way of generating data based on denoising score matching with Annealed Langevin sampling (DSM-ALS). The paper showed tha…
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Hash: A free, online platform for modeling
Sometimes simulating complex systems is the best way to understand them.
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The Case for Bayesian Deep Learning
The Case for Bayesian Deep Learning Andrew Gordon Wilson Abstract Bayesian inference is especially compelling for deep neural networks. The key distinguishing property of a Bayesian approach is margin
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VSCode on Google Colab
Learn how to setup and use VSCode as an IDE on Google Colab and Kaggle.
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Building My Own Chess Engine
Exploring the computational complexity of chess. Code snippets in Python so you can do the same.
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What is GPT-3? written in layman's terms
Daily, snackable writings to spur changes in thinking. Building a blueprint for a better brain by tinkering with the code.
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Why Bayesian Stats Needs Monte-Carlo Methods
Bayesian statistics rely heavily on Monte-Carlo methods. A common question that arises is “isn’t there an easier, analytical solution?” This post explores a bit more why this is by breaking down the analysis of a Bayesian A/B test and showing how tricky the analytical path is and exploring more of t
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A complete 4-year course plan for an AI undergraduate degree
I describe in-depth the courses necessary for a 4-year undergraduate degree in artificial intelligence, assuming you step onto campus tomorrow.
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On moving from statistics to machine learning, the final stage of grief (2019)
I’ve spent the last few months preparing for and applying for data science jobs. It’s possible the data science world may reject me and my lack of both experience and a credential above…
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I asked GPT-3 to make a presentation for me
Here's what it came up with, read by yours truly.
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An Opinionated Guide to Machine Learning Research
I originally wrote this guide in back in December 2017 for the OpenAI Fellows program
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A visual explanation for regularization of linear models
A deep dive into exactly how regularization works, using visualizations rather than (just) mathematics. This article also dispels some myths and answer some important questions about Lasso and Ridge regression.
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AI that mistook a bald head for a soccer ball
Second-tier Scottish football club Inverness Caledonian Thistle doesn’t have a camera operator for matches at their stadium so the club uses an AI-controlled ca
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Object Detection at 1840 FPS with TorchScript, TensorRT and DeepStream
In this article we take performance of the SSD300 model even further, leaving Python behind and moving towards true production deployment technologies: TorchScript, TensorRT and DeepStream. We also identify and understand several limitations in Nvidia’s DeepStream framework, and then remove them by modifying how the…