Distill

“Distill” is a comprehensive source that delves into the intricacies of Python programming, AI applications, and machine learning models. It explores the significance of speed in Python, the importance of short-term memory in AI applications, and the role of data augmentation in machine learning. The document discusses the challenges faced in enterprise RAG implementations and emphasizes the need for understanding data beyond just indexing. It also highlights the critical aspects of building a fault-tolerant enterprise analyst using contract engineering and self-healing architectures. Overall, “Distill” provides valuable insights into various aspects of programming, AI, and machine learning.

Visit Distill

Deconvolution and Checkerboard Artifacts

Distill

When we look very closely at images generated by neural networks, we often see a strange checkerboard pattern of artifacts. It’s more obvious in some cases than others, but a large fraction of recent ...

How to Use t-SNE Effectively

Distill

Although extremely useful for visualizing high-dimensional data, t-SNE plots can sometimes be mysterious or misleading. By exploring how it behaves in simple cases, we can learn to use it more effecti...

Attention and Augmented Recurrent Neural Networks

Distill

Recurrent neural networks are one of the staples of deep learning, allowing neural networks to work with sequences of data like text, audio and video. They can be used to boil a sequence down into a h...

Why Momentum Really Works

Distill

Here’s a popular story about momentum [1, 2, 3] : gradient descent is a man walking down a hill. He follows the steepest path downwards; his progress is slow, but steady. Momentum is a heavy ball rol...

A Gentle Introduction to Graph Neural Networks

Distill

This article is one of two Distill publications about graph neural networks. Take a look at Understanding Convolutions on Graphs to understand how convolutions over images generalize naturally to conv...

Understanding Convolutions on Graphs

Distill

This article is one of two Distill publications about graph neural networks. Take a look at A Gentle Introduction to Graph Neural Networks for a companion view on many things graph and neural network...

Distill Hiatus

Distill

Over the past five years, Distill has supported authors in publishing artifacts that push beyond the traditional expectations of scientific papers. From Gabriel Goh’s interactive exposition of momentu...

Understanding RL Vision

Distill

In this article, we apply interpretability techniques to a reinforcement learning (RL) model trained to play the video game CoinRun . Using attribution combined with dimensionality reduction as in , ...

Communicating with Interactive Articles

Distill

Computing has changed how people communicate. The transmission of news, messages, and ideas is instant. Anyone’s voice can be heard. In fact, access to digital communication technologies such as the ...

Thread: Differentiable Self-organizing Systems

Distill

Self-organisation is omnipresent on all scales of biological life. From complex interactions between molecules forming structures such as proteins, to cell colonies achieving global goals like explora...

Exploring Bayesian Optimization

Distill

Many modern machine learning algorithms have a large number of hyperparameters. To effectively use these algorithms, we need to pick good hyperparameter values. In this article, we talk about Bayesian...

Visualizing Neural Networks with the Grand Tour

Distill

The Grand Tour is a classic visualization technique for high-dimensional point clouds that projects a high-dimensional dataset into two dimensions. Over time, the Grand Tour smoothly animates its proj...