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Unpacking How Ad Ranking Works at Pinterest
Aayush Mudgal describes how Pinterest serves advertisements. He discussed in detail how Machine Learning is used to serve ads at large scale. He went over ads marketplaces and the ad delivery funnel, the ad serving architecture, and two of the main problems: ad retrieval and ranking. Finally, he discussed some of the challenges and solutions for training and serving large models.
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AutoML: the Promise vs. Reality According to Practitioners
Automation to improve machine learning projects comes from a noble goal, but true end-to-end automation is not available yet. As a collection of tools, AutoML capabilities have proven value but need to be vetted more thoroughly. Findings from a qualitative study of AutoML users suggest the future of automation for ML and AI rests in the ability for us to realize the potential of AutoMLOps.
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Challenges of Human Pose Estimation in AI-Powered Fitness Apps
In this article, the author discusses the human pose estimation solution powered by AI technologies and the challenges faced in online fitness apps which use the pose estimation to predict the position of the human body based on an image or a video containing a person.
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The First Wave of GPT-3 Enabled Applications Offer a Preview of Our AI Future
The first wave of GPT-3 powered applications are emerging. After priming of only a few examples, GPT-3 could write essays, answer questions, and even generate computer code! Furthermore, GPT-3 can perform algebraic calculations and language translations despite never being taught such concepts. However, GPT-3 is a black box with unpredictable outcomes. Developers must use it responsively.
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Article Series: An Introduction to Machine Learning for Software Developers
Get an introduction to some powerful but generally applicable techniques in machine learning for software developers. These include deep learning but also more traditional methods that are often all the modern business needs. After reading the articles in the series, you should have the knowledge necessary to embark on concrete machine learning experiments in a variety of areas on your own.
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Anomaly Detection for Time Series Data with Deep Learning
This article introduces neural networks, including brief descriptions of feed-forward neural networks and recurrent neural networks, and describes how to build a recurrent neural network that detects anomalies in time series data. To make our discussion concrete, we’ll show how to build a neural network using Deeplearning4j, a popular open-source deep-learning library for the JVM.