Jeremy Jordan

A few weeks ago, I was swimming laps after work, reflecting on my day.

Over the past few years, we've seen an incredible rate of scaling when it comes to training deep neural networks.

In a previous post, we discussed a key innovation in sequence to sequence neural network architectures: the attention mechanism.

In this blog post, we'll discuss a key innovation in sequence-to-sequence model architectures: the attention mechanism.

Let's say you want to deploy a recommender system at your company.

This page contains a quick reference for writing Terraform configuration.

This blog post aims to provide a simple, open-source solution for monitoring ML systems.

Working as a core maintainer for PyTorch Lightning, I've grown a strong appreciation for the value of tests in software development.

This blog post will provide an introduction to Kubernetes so that you can understand the motivation behind the tool, what it is, and how you can use it.

Previously, I wrote about organizing machine learning projects where I presented the framework that I use for building and deploying models.

Jump to: Overview Evolving a hidden state over time Common structures of recurrent networks Bidirectionality Limitations Further reading Overview Previously, I've written about feed-forward neural networks as a generic function approximator and convolutio…

Jump to: What is nearest neighbors search?

The goal of this document is to provide a common framework for approaching machine learning projects that can be referenced by practitioners.

In this post, I'll discuss an overview of deep learning techniques for object detection using convolutional neural networks.

When evaluating a standard machine learning model, we usually classify our predictions into four categories: true positives, false positives, true negatives, and false negatives.