In summer 2020 I had a small Python web app around one of my favorite datasets: 300 chocolates that my hunsband and me had tried over the years with reviews and ratings and a machine learning model that predicts how much we may like an unknown chocolate.
Transitioning to another SQL database? This blog post is for you. Shifting from one SQL dialect to another can be a journey full of surprises. While the basic syntax (SELECT FROM WHERE) is similar, there are important differences, that will make your queries slow, fast, fail or worse: fail silently!
Search engines rely on models, which rank the matching results for a given user query. These models optimize the order of items. They learn how to rank items in a result list, therefore the name Learning-to-Rank (LTR) models.
You have kafka as your message broker up and running and you may wonder: In which format should I send my data around? Maybe the string format pops up in your mind. Why not just put all fields into a long string and separate them with a comma?
In this blogpost you will get a basic understanding about message brokers. We will look at two very popular message brokers, Kafka and RabbitMQ, and learn, how they handle messages.
This post will teach you the inution of REST APIs and how you can use them to get interesting datasets for your data projects. First, we will look at the four components of a request. In the second part of this blogpost, we will go through one example and access the coingecko API via curl.
In many scenarios, such as a google search or a product recommendation in an online shop, we have tons of data and limited space to display it. We cannot show all the products of an online shop to the user as a possible next best offer. Neither would a user want to scroll through all the pages indexed by a search engine to find the most relevant page that matches his search keywords. The most relevant content should be on top. Learning to rank (LTR) models are supervised machine learning models that attempt to optimize the order of items. So compared to classification or regression models, they do not care about exact scores or predictions, but the relative order. LTR models are typically applied in search engines, but gained popularity in other fields such as product recommendations as well.
I was recently invited to join a panel discussion among developers to dispel the myth of the typical BS Buzzword Bingo around machine learning and AI. In this blog post, I will share some buzzwords we talked about with a little description and links. Ooops, I already used some buzzwords. So let’s start.
Humans intuitively understand the meaning of words: Which words are similar, opposites or related to each other? But our machine learning models do not have this intuition. Word embeddings are numeric vectors that represent text. These vectors are learned through neural networks. The objective when creating these embedding vectors is to capture as much “meaning” as possible: Related words should be closer together than unrelated words. Also, they should be able to preserve mathematical relationships between words such as
In this blogpost I will share some tips for working with Jupyter Notebooks. Those tips greatly improved my productivity when working with Jupyter Notebooks and I wish someone would have told me earlier. The two main topics of this post are extensions and magic commands.
