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Python vs. Rust for Neural Networks
Comparing a simple neural network in Rust and Python
New
The latest blog posts to take off on Hacker News. 3,092 posts.
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Siri, Privacy, and Trust
We should expect Apple to lead the industry on this front, but in fact, they’re far behind.
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What’s the difference between statistics and machine learning?
I had an interesting discussion at work today (among people I think would all call themselves statisticians!) about the distinction(s) between statistics and machine learning. It is something I am …
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Introduction to Bayesian Filter
Restate Some Basic Concepts and Math in Bayesian Filter
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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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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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Adventures Building a Self-Driving RC Car
Having read some amazing books on Machine Learning, I had been looking for opportunities to apply ML from first principles in the real world. That was what got me curious about the wonderful Donkey Car project. The project is essentially a how-to guide to building your own RC car which can drive itself around a track…
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Interpretable Machine Learning
Machine learning is part of our products, processes, and research. But computers usually don’t explain their predictions , which can cause many problems, ranging from trust issues to undetected bugs. This book is about making machine learning models and their decisions interpretable.
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AIs Named by AIs
Neural networks can be good at naming things, I’ve discovered. Recently I’ve been experimenting with a neural network called GPT-2, which OpenAI trained on a huge chunk of the internet. Thanks to a colab notebook implementation by Max Woolf, I’m able to fine-tune it on specific lists of data -
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Machine learning systems are stuck in a rut
Machine learning systems are stuck in a rut Barham & Isard, HotOS’19 In this paper we argue that systems for numerical computing are stuck in a local basin of performance and programmabil…
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Logistic Regression from Bayes’ Theorem
In this post we’ll explore how we can derive logistic regression from Bayes’ Theorem. Starting with Bayes’ Theorem we’ll work our way to computing the log odds of our problem and the arrive at the inverse logit function. After reading this post you’ll have a much stronger intuition for how logistic
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Implementing a Convolutional Neural Network Using Only NumPy
A simple walkthrough of deriving backpropagation for CNNs and implementing it from scratch in Python.
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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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Notes on Weight Initialization for Deep Neural Networks
Neural networks involve long sequence of multiplications, usually between a matrix and a vector, say \(a*x\). The result of this sequence of multiplications will either have a huge magnitude or be reduced to 0. We can divide \(a\) by a number ( scaling_factor ) to scale down its magnitude to the right level. Proper…
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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…
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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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A Recipe for Training Neural Networks
Musings of a Computer Scientist.
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Notes on AI Bias
Machine learning is the new centre of tech, and like all big new things there are issues. ‘AI bias’ is much-discussed right now: machine learning finds patterns but sometimes it finds the wrong one, and it can be hard to tell.
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Graph Convolutional Networks (2016)
Many important real-world datasets come in the form of graphs or networks: social networks, knowledge graphs, protein-interaction networks, the World Wide Web, etc. (just to name a few). Yet, until recently, very little attention has been devoted to the generalization of neural...
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Deep Learning in Clojure from Scratch to GPU: Learning a Regression
A great moment has arrived. We are going to apply our neural networks implementation to a regression problem. The network is going to learn a known function,...
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Photos from Crude Sketches: Nvidia's GauGAN Explained Visually
In this article I'll be explaining how GauGAN converts simple blobs of color into photorealistic paintings of landscapes.