Archive | Machine Learning

Teach Don’t Document

Yesterday, HuggingFace launched their “HuggingFace course” — A self-paced introduction to their NLP library along with short explainers. My first reaction was “oh great, another tutorial in the glut of Deep Learning educational resources”, but since it was from HuggingFace, I put my opinion aside and gave it a closer look. The more I looked, […]

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Unstoppable AI Flywheels and the Making of the New Goliaths

TL;DR: AI creates engines for relentless optimization at all levels. Read the article to figure out how and its consequences to what you’re doing. Some time ago I wrote about how everything is a model when reviewing a paper from Kraska et al (2017) where they show how traditional CS data structures like B-Tree indexes, […]

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Differentiable Dynamic Programs and SparseMAP Inference

Two exciting NLP papers at ICML 2018! ICML 2018 accepts are out, and I am excited about two papers that I will briefly outline here. I think both papers are phenomenally good and will bring back structured prediction in NLP to modern deep learning architectures. Differentiable Dynamic Programming for Structured Prediction and Attention Arthur Mensch […]

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Everything is a Model

TLDR: I review a recent systems paper from Google, why it is a wake-up call to the industry, and the recipe it provides for nonlinear product thinking. Here, I will be enumerating my main takeaways from a recent paper, “The Case for Learned Index Structures” by Tim Kraska, Alex Beutel, Ed Chi, Jeffrey Dean, and […]

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Is BackPropagation Necessary?

In the previous post, we saw how the backprop algorithm itself is a bottleneck in training, and how the Synthetic Gradient approach proposed by DeepMind reduces/avoids network locking during training. While very clever, there is something unsettling about the solution. It seems very contrived, and definitely resource intensive.  For example, a simple feed forward network under the […]

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Synthetic Gradients .. Cool or Meh?

Synthetic what now? DeepMind recently published about Synthetic Gradients. This post is about that — what they are, and does it make sense for your average Deep Joe to use it. A Computational Graph is the best data structure to represent deep networks. (D)NN training and inference algorithms are examples of data flow algorithms, and […]

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