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BERT, but in Italy — image by authorMany of my articles have been focused on BERT — the model that came and dominated the world of natural language processing (NLP) and marked a new age for language models. For those of you that may not have used transformers models (eg what BERT is) before, the process looks a little like this:
The principle of SOLID coding is an acronym originated by Robert C. Martin, and it stands for five different conventions of coding. If you follow, these principles you can improve the reliability of your code by working on its structure and its logical consistency.
Geometric Deep Learning is an attempt for geometric unification of a broad class of ML problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled…
Image by Lorenzo Cafaro from PixabayUsage of good grammar and correctly spelled words helps you to write and communicate clearly and get what you want. Whether you are working on an article, essay, or email, presenting your ideas with clear and correct language makes a good impression on your readers. Often while typing emails, essays, articles, etc one makes a…
Full code and simulated dataset are posted on my Github repo: https://github.com/sibylhe/mmm_stan The methodology of this project is based on this paper by Google, but is applied to a more complicated, real-world setting, where 1) there are 13 media channels and 46 control variables; 2) models are built in a stacked way. 1. IntroductionMarketing Mix Model, or Media Mix Model (MMM) is used by adver
Photo by Adam Nowakowski on UnsplashIf you Google “modern data platform”, you’ll immediately be bombarded with advertisements and lots of companies professing that they are the one true data platform. Not so helpful, right? So what the heck is a modern data platform? What does that even mean, and what does it look like in 2021? The short answer: a modern data platform is a collection of tools and
IntroductionMachine learning models are exciting and powerful, but they aren’t very useful by themselves. Once a model is complete, it likely has to be deployed before it can deliver any sort of value. As well, being able to deploy a preliminary model or a prototype to get feedback from…
Distance Measures. Image by the author.Many algorithms, whether supervised or unsupervised, make use of distance measures. These measures, such as euclidean distance or cosine similarity, can often be found in algorithms such as k-NN, UMAP, HDBSCAN, etc.
Image courtesy of Andrey_Kuzmin on ShutterstockAs companies increasingly leverage data to power digital products, drive decision making, and fuel innovation, understanding the health and reliability of these most critical assets is fundamental. For decades, organizations have relied on data catalogs to power data governance. But is that enough? Debashis Saha, VP of Engineering at AppZen, formerly
MLOps can be difficult for teams to get a grasp of. It is a new field and most teams tasked with MLOps projects are currently coming at it from a different background. It is tempting to copy an approach from another project. But the needs of MLOps projects can vary greatly. What is needed is to understand the specific needs of each MLOps project. This requires understanding the types of MLOps need
Every day, businesses deal with large volumes of unstructured text. From customer interactions in emails to online feedback and reviews. To deal with this large amount of text, we look towards topic modeling. A technique to automatically extract…
Jupyter Notebooks in Microsoft Excel. Image by the author.It used to be an “either/or” choice between Excel and Python Jupyter Notebooks. With the introduction of the PyXLL-Jupyter package now you can use both together, side by side. In this article I’ll show you how to set up Jupyter Notebooks running inside Excel. Share data between the two and even call Python functions written in your Jupyter
Do you know what kind of sensitive data your organization holds? Are you keeping track of every change applied across all your tables and columns? Are you confident to answer questions an auditor may have on data regulations? Having an auditor knocking in your door is not the scariest thing, data breaches can be way scarier! From fines to customer loss and legal ramifications — the consequences ca
The keyword extraction is one of the most required text mining tasks: given a document, the extraction algorithm should identify a set of terms that best describe its argument. In this tutorial, we are going to perform keyword extraction with five different approaches: TF-IDF, TextRank, TopicRank, YAKE!, and KeyBERT. Let’s see who performs better!
An updated version of this story is available on the Platypush blog. Some of you may have noticed that it’s been a while since my last article. That’s because I’ve become a dad in the meantime, and I’ve had to take a momentary break from my projects to deal with some parental tasks that can’t (yet) be automated.
The illustrations are best viewed on the Desktop. A Colab version can be found here (thanks to Manuel Romero!). Changelog: 30 Dec 2022 — Use Medium’s new code block for syntax highlighting 12 Jan 2022 — Improve clarity 5 Jan 2022 — Fix…
120+ Data Scientist Interview Questions and Answers You Should Know in 2021 Interview Questions from Facebook, Yelp, Amazon, Google, Apple, Netflix, and More
Few years back, it was very difficult to extract Subjects/Topics/Concepts of thousands of unannotated free text documents. Best and simple way was to make some human sit, go thru each articles, understand and annotate Topics. Indeed it was time consuming and prone to subjectivity of perception we humans have. Although many attempts were made in past with simple algorithms like pLSA to treat this a
FastAPI Logo — https://fastapi.tiangolo.comWhat do you like best about being a data scientist? It’s definitely modeling and fine-tuning for optimal results. But what does it mean to be a good model if it’s never used or never deployed? To produce a machine learning model, the typical approach is to wrap it in a REST API and use it as a…
Image by Mudassar Iqbal from Pixabay, Edited using PixlrExploratory data analysis (EDA) is an approach to analyze the data and find patterns, visual insights, etc. that the data set is having, before proceeding to model. One spends a lot of time doing EDA to get a better understanding of…
By geralt at pixabayA common task for time series machine learning is classification. Given a set of time series with class labels, can we train a model to accurately predict the class of new time series?
To date, there are a lot of books out there about Natural Language Processing that you could learn from. However, choosing the right book for yourself might be intimidating since there is just so much! This post provides a list of the top books I personally recommend to…
Introductionaltair is an interactive visualization library. It offers a more consistent API. This is how the authors describe the library. Declarative statistical visualization library for Python What does it mean is that it focuses on what to plot instead of how to plot, and you can easily…
I am pleased to announce the open-source Python package PyTorch Forecasting. It makes time series forecasting with neural networks simple both for data science practitioners and researchers. Why is accurate forecasting so important?Forecasting time series is important in many contexts and highly relevant to machine learning practitioners. Take, for example, demand forecasting from which many use c
Plugins can modify and extend a lot of aspects of pylint, including how the output is done. This screenshot of a pytest run with pytest-sugar was taken by Martin Thoma. Pytest is extensible and has plenty of plugins. You don’t need to use any of them, but you might find some very useful. I love this because you have an easy time to get started with unit testing, while still finding amazing stuff w
BigQuery offers the ability to load a TensorFlow SavedModel and carry out predictions. This capability is a great way to add text-based similarity and clustering on top of your data warehouse. Follow along by copy-pasting queries from my notebook in GitHub. You can try out the queries in the BigQuery console or in an AI Platform Jupyter notebook. Text embeddings are useful for document similarity
A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start? The funny thing was that the path that I imagined was completely different from that one that I actually did when I was starting. I’m aware that we all learn in different ways. Some prefer videos, others are ok with just books and a lot of people need to pay for
(Image by Author)IntroductionNatural language processing (NLP) is an intimidating name for an intimidating field. Generating useful insight from unstructured text is hard, and there are countless techniques and algorithms out there, each with their own use-cases and complexities. As a developer with minimal NLP exposure, it can be difficult to know which methods to use, and how to implement them.
Building search systems is hard. Preparing them to work with machine learning is really hard. Developing a complete search engine framework integrated with AI is really really hard. So let’s make one. ✌️ In this post, we’ll build a search engine from scratch and discuss on how to further optimize results by adding a machine learning layer using Kubeflow and Katib. This new layer will be capable of
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