Contextual bandit models are a popular approach for personalizing the user experience by recommending relevant products. However, these models become challenging to train and evaluate when the number of actions, or products, in the recommendation pool become large. In this blog post, we will explore the difficulties associated with using contextual bandit models in large action spaces and propose potential solutions to overcome these challenges. One of these solutions was recently launched into production at Instacart.
Wow, in my last article I already showed you how to set up the Vicuna model on your local computer, but the results were not as good as expected.
For this reason, I created a fork and basically merged two repositories to get the Vicuna model up and running, and what can I say, the responses and especially the quality of the responses are insane. Forget alpaca, seriously! You don’t believe me?
Harness the power of ChatGPT to quickly and easily generate Terraform code for your infrastructure needs. This tutorial will walk you through the process step-by-step
Link
The MERGE command or statement in standard SQL is used to perform incremental load. i.e. load only new set of records into target table. With the help of SQL MERGE statement, you can perform UPDATE and INSERT simultaneously based on the merge condition.
Link
Learn how to use the OpenAI API to create five projects, including a ChatGPT clone, a DALL-E Image Creator, and a SQL Generator. This is a dive deep into the world of the OpenAI API, exploring its diverse capabilities and potential applications.
Link