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Why TensorFlow Will Stand Out on Your Resume in 2020
You likely hear about TensorFlow in the machine & deep learning circles for quite a while now, and for good reason. This Google-developed framework excels where many other libraries don’t, such as with its scalable nature designed for production deployment. With that, here are just a... Read more
Data – Driven AI: A Case Study Approach Using a Churn Reduction Example
0.1 Introduction and Background to the Case Study: Framingham Heart Study In the early 1990s heart disease became the leading cause of death. The effects of smoking, cholesterol and obesity on heart disease and stroke was unknown. High blood pressure was seen as an inevitable consequence... Read more
Optimizing DoorDash’s Marketing Spend with Machine Learning
Originally posted here by Doordash, reposted with permission. Over a hundred years ago, John Wanamaker famously said “Half the money I spend on advertising is wasted; the trouble is, I don’t know which half”. This quote still resonates today. Marketers are always trying to find ways to get... Read more
Using a Human-in-the-Loop to Overcome the Cold Start Problem in Menu Item Tagging
Originally posted here at Doordash, reposted with permission. Companies with large digital catalogs often have lots of free-text data about their items, but very few actual labels, making it difficult to analyze the data and develop new features. Building a system that can support machine learning... Read more
Retraining Machine Learning Models in the Wake of COVID-19
Originally posted here by Doordash, with permission. The advent of the COVID-19 pandemic created significant changes in how people took their meals, causing greater demand for food deliveries. These changes impacted the accuracy of DoorDash’s machine learning (ML) demand prediction models. ML models rely on patterns... Read more
Leveraging Causal Modeling to Get More Value from Flat Experiment Results
Article originally posted here by Doordash, with permission. A/B tests and multivariate experiments provide a principled way of analyzing whether a product change improves business metrics. However, such experiments often yield flat results where we see no statistically significant difference between the treatment and the control.... Read more
Big Data Pipelines: Past, Present, and Future
When data scientists convert research work to production machine learning tasks, a key challenge is how to schedule and organize computations so that results are reliably available and consistently accurate. This is the impetus for the world of tools that offer job scheduling and pipelining functionality.... Read more
Teaching KNIME to Play Tic-Tac-Toe
In this blog post I want to introduce some basic concepts of reinforcement learning, some important terminology, and show a simple use case where I create a game playing AI in KNIME Analytics Platform. After reading this, I hope you’ll have a better understanding of the... Read more
Learn NLP the Stanford Way — Lesson 1
The AI area of Natural Language Processing, or NLP, throughout its gigantic language models — yes, GPT-3, I’m watching you — presents what it’s perceived as a revolution in machines’ capabilities to perform the most distinct language tasks. Due to that, the perception of the public... Read more
The State of Enterprise NLP in 2020
2020 has been a unique year for public health, professional life, the economy, and just about every other aspect of daily life. While some doors are closing, and others are pivoting their business models, businesses that haven’t taken a hit are a rare breed. Despite this,... Read more