In this blog post, I'll cover a couple of techniques used for approximate nearest neighbors search. This post will not cover approximate nearest neighbors methods exhaustively, but hopefully you'll be able to understand how people generally approach this problem and how to apply these techniques| Jeremy Jordan
In this post, I'll discuss an overview of deep learning techniques for object detection using convolutional neural networks. Object detection is useful for understanding what's in an image, describing both what is in an image and where those objects are found.| Jeremy Jordan
In this blog post, we'll discuss techniques such as data and model parallelism which allow us to distribute the model training process across a large cluster of machines.| Jeremy Jordan
In my first post on neural networks, I discussed a model representation for neural networks and how we can feed in inputs and calculate an output. We calculated this output, layer by layer, by combining the inputs from the previous layer with weights for each neuron-neuron connection. I mentioned that| Jeremy Jordan
In my introductory post [https://www.jeremyjordan.me/neural-networks-representation/] on neural networks, I introduced the concept of a neural network that looked something like this. As it turns out, there are many different neural network architectures [http://www.asimovinstitute.org/neural-network-zoo/], each with its own set of benefits. The architecture| Jeremy Jordan
When evaluating a standard machine learning model, we usually classify our predictions into four categories: true positives, false positives, true negatives, and false negatives. However, for the dense prediction task of image segmentation, it's not immediately clear what counts as a "true positive" and, more generally, how we can evaluate| Jeremy Jordan
The attention mechanism allows us to merge a variable-length sequence of vectors into a fixed-size context vector. What if we could use this mechanism to entirely replace recurrence for sequential modeling? This blog post covers the Transformer architecture which explores such an approach.| Jeremy Jordan
In this blog post, we'll discuss a key innovation in sequence-to-sequence model architectures: the attention mechanism. This architecture innovation dramatically improved model performance for sequence-to-sequence tasks such as machine translation and text summarization. Moreover, the success of this attention mechanism led to the seminal paper, "Attention Is All You Need"| Jeremy Jordan
Let's say you want to deploy a recommender system at your company. A typical architecture might include a set of inference servers to run your embedding and ranking models, an approximate nearest neighbor index to select a set of candidate items that match your query, a database to retrieve features| Jeremy Jordan
This page contains a quick reference for writing Terraform configuration.| Jeremy Jordan
This blog post aims to provide a simple, open-source solution for monitoring ML systems. We'll discuss industry-standard monitoring tools and practices for software systems and how they can be adapted to monitor ML systems.| Jeremy Jordan
In this blog post, we'll cover what testing looks like for traditional software development, why testing machine learning systems can be different, and discuss some strategies for writing effective tests for machine learning systems. We'll also clarify the distinction between the closely related| Jeremy Jordan
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. In a follow-up post, I'll discuss how we can leverage Kubernetes to power data science workloads using more concrete (data science) examples.| Jeremy Jordan
Previously, I wrote about organizing machine learning projects where I presented the framework that I use for building and deploying models. However, that framework operates on the implicit assumption that you already know generally what your model should do.| Jeremy Jordan
In this post, I'll discuss a third type of neural networks, recurrent neural networks, for learning from sequential data. For some classes of data, the order in which we receive observations is important. As an example, consider the two following sentences:| Jeremy Jordan
The goal of this document is to provide a common framework for approaching machine learning projects that can be referenced by practitioners. If you build ML models, this post is for you.| Jeremy Jordan
In this post, I'll discuss how to use convolutional neural networks for the task of semantic image segmentation. Image segmentation is a computer vision task in which we label specific regions of an image according to what's being shown.| Jeremy Jordan