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New
The latest blog posts to take off on Hacker News. 101 posts.
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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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Building a simple neural net in Java
Building a simple neural net in Java.
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What I wish someone had told me about tensor computation libraries
I get confused with tensor computation libraries (or computational graph libraries, or symbolic algebra libraries, or whatever they’re marketing themselves as these days). I was first introduced to PyTorch and TensorFlow and, having no other reference, thought they were prototypical examples of tensor computation…
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AlphaFold2 at CASP14: “It feels like one’s child has left home.”
The past week was a momentous occasion for protein structure prediction, structural biology at large, and in due time, may prove to be so for the whole of life sciences. CASP14, the conference for …
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InstaHide Disappointingly Wins Bell Labs Prize, 2nd Place
[What follows are my thoughts on some recent research in machine learning privacy. These are my thoughts, and do not represent those of others.]
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Scaling Down Deep Learning
A blog by Sam Greydanus
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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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What is protein folding? A brief explanation
AlphaFold from Google DeepMind is said to solve the protein folding problem. What is that, and why is it hard?
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I asked GPT-3 for the question to “42”
Can GPT-3 compute the ultimate question about life, the Universe and everything?
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You might not need machine learning
Machine learning is a trendy topic, so naturally it’s often used for inappropriate purposes where a simpler, more efficient, and more reliable solution suffices. The other day I saw an illustrative and fun example of this: Neural Network Cars and Genetic Algorithms . The video demonstrates 2D cars driven by a neural…
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Introduction to Linear Algebra for Applied Machine Learning with Python
Linear algebra is to machine learning as flour to bakery: every machine learning model is based in linear algebra, as every cake is based in flour . It is not the only ingredient, of course. Machine learning models need vector calculus, probability, and optimization, as cakes need sugar, eggs, and butter. Applied…
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Cool Machine Learning Books
awhile ago i posted my list of cool machine learning books , but it's been awhile so it's probably time to update it...
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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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Image Scaling Attacks
This post is part of a series about machine learning and artificial intelligence. Click on the blog tag “huskyai” to see related posts. Overview: …
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Julia GPU
Lambdaclass's blog about distributed systems, machine learning, compilers, operating systems, security and cryptography.
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Why deep learning works even though it shouldn’t
Why models always get better when they are bigger and deeper, even when the amount of data they consume stays the same or gets smaller.
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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…
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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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Gradient Boosted Decision Trees
From zero to gradient boosted decision trees
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Nvidia announces Jetson Nano 2GB, a single board computer
Using Python 3.6, OpenCV, Dlib and the face_recognition module
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Why neural networks struggle with the Game of Life
The Game of Life is based on very simple rules, but artificial neural networks struggle to learn its rules through the classic deep learning training process.
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Nvidia’s Integration Dreams
Nvidia’s acquisition of ARM only makes sense from a financial perspective, unless you buy Jensen Huang’s datacenter dreams.
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Differentiable Dithering
Problem Let's say we want to reduce the number of colors in an image. For example consider the picture of fruit below:
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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