Petar Savov

Following on from last week's article, this week I cover Activation Functions

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I have published two datasets this week. First one is consolidate, select and reformatting of a bunch of top Thai instruct dataset Second is Slim Orca partially(once state of the art dataset) translate to Thai using gpt-4o-mini https://lnkd.in/gq3pG_Ru https://lnkd.in/gMSWi_MT

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Delivering personalised recommendations is key to engaging our customers, but traditional models can miss the complexity of user-content interactions. By integrating graph neural networks (GNNs), we’re exploring a cutting-edge approach to better predict clicks and enhance the shopping experience. In her post, Senior Applied Scientist Mariia Bulycheva explains how GNN features were integrated into the models powering the Zalando Homepage: https://bit.ly/4fNnQZC #ZalandoTech #InsideZalando #ZalandoScience #RecSys

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"...embeddings enable machine learning models to find similar objects." Read more of Gregory Hodum's article on embeddings https://lnkd.in/eFs6hhAP #ai #softwaredevelopment #embeddings #machinelearning

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🧑‍💻 Don't forget to check out Simon Prince's latest research blog, Bayesian Neural Networks, where he explores Bayesian learning for neural networks from the parameter perspective. The piece is part VI in a series where machine learning is considered from different viewpoints. Read the blog here 🔗 https://lnkd.in/gnUhDXiF Missed Parts I - V? Find them below. Part I 🔗 https://lnkd.in/gxzpKMFb Part II 🔗 https://lnkd.in/g7haGzse Part III 🔗 https://lnkd.in/gbWWj6tZ Part IV 🔗 https://lnkd.in/gUstQyUv Part V 🔗 https://lnkd.in/gQcdE_ds Keep an eye out for the final installment of the series, coming soon. 👀 #ML #machinelearning #AI #AIinCanada #research #researchtutorial #neuralnetworks #Bayesianmethods #neuralnetworks

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How often does an opportunity come to work on a greenfield platform like Pydantic Logfire? Build a database that will scale (if we get it right) to petabyte scale and change how develoeprs understand their applications.

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What’s the difference between null and NaN in your data? Let's ask Cameron Riddell! In this week's Cameron's Corner, he breaks down the nuances of missing values in data analysis, covering how null and NaN are treated differently across popular Python libraries like Pandas and Polars. If you’ve ever wondered why your calculations go sideways or why certain rows mysteriously disappear, this post is for you! Check it out: https://lnkd.in/g3-CA7QA #python #pandas #Polars #pythonprogramming #blogpost #DataScience #MissingData #NaN #Null #datavisualization

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Recently, I got to experiment on developing a Text vectorisation service for a continuous inflow of Tickets text data. We used Celery and E5 model to test the performance and tried to optimise on throughput. I have penned down my learnings and the issues I faced during the experiment Text vectorisation service using Celery and E5 model

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Instead of catching up on the new Big Bang series, how about making some mula? Freelance work - for *SENIOR Python devs* UP TO R850 per hour and you would need experience with LLM's. ( If I have to tell you what LLM is then it's not you 🤣) min 4 hours max 20 hours a week. Hit me up quick - I need 2 chaps to start asap... first come first serve. [email protected] #python #chatgtp #Ai

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Latest AI/ML observation from my conversations today... How AI/ML problem solving is often described: Gather data -> clean data -> EDA -> feature engineering -> train model (often the fanciest one) -> test model -> post-processing -> and so on. How AI/ML problem solving really happens: Understand the physics, chemistry and/or fundamental nature of the problem -> read about how others have already tried AI/ML in the space -> define and refine the AI problem statement multiple times -> define an approach and hypothesis -> and so on.

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What if your OpenAI API calls were 100% reliable? This is the claim from the Structured Outputs announcement yesterday. Here is the excerpt: "On our evals of complex JSON schema following, our new model gpt-4o-2024-08-06 with Structured Outputs scores a perfect 100%. In comparison, gpt-4-0613 scores less than 40%." I have been using libraries like instructor to get structured output, but this might mean that all you need is the API now. What do you think? Link: https://buff.ly/46SD2Cd

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Really happy to share that our work on “Learning Metrics that Maximise Power for Accelerated A/B-tests” will appear in the Applied Data Science track of #KDD2024. We address a common problem in industry: how can we minimise type-II errors in online controlled experiments? Can we simply learn a metric combination that directly minimises this quantity? This project started out as “let’s implement the ideas from this great paper by Eugene Kharitonov et al.”, but we ended up proposing some extensions to their seminal work that seem to be quite effective, and are now used across ShareChat. Proud to have this work out there, let us know what you think. Additionally, we were assigned an Area Chair that clearly cared: initial low-confidence reviews prompted them to invite more reviewers (we got 8!), and the AC themselves correctly grilled our rebuttals to point out arguments they deemed insufficient. It’s easy to be positive about peer review when your paper gets accepted, but it seems there are still some unsung heroes out there... Work with Aleksei U.. ACM SIGKDD & Annual KDD Conference

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In this crash course, Chanin Nantasenamat takes you from zero to data app in less than half an hour! ⏱️ We’ll cover everything from development to deployment to debugging ✨ So what're you waiting for? It's time to start Streamlit-ing!

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Our episode on the The Data Exchange Podcast with our fantastic host Ben Lorica 罗瑞卡 , and featuring my great colleague Hima Patel , on data preparation for LLM applications (RAG, fine-tuning, instruct-tuning, and pre-training) using the Data Prep Kit is out! Data Prep Kit is the open-source (Apache 2.0 license) framework and transforms for language and code, which we used in IBM Research to prepare the data for the training of #IBM 's #Granite models ( https://lnkd.in/eUvZVK2t ). Running at any-scale, from a laptop to a datacenter and handling from a few pages of text to hundreds of billions of documents, it is available in pure-#Python , #Ray , #Spark and with #KubeFlow pipelines. Feel free to check it out here: https://lnkd.in/ehGjpbzG #IBMResearch #AI #GenAI #LLM

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Week 4 of DataTalksClub LLM-zoomcamp was rich in content. This week the focus was: 1) Setting up an LLM powered Streamlit app. The app provides users the capability to ask questions. We capture feedback from the user as part of the monitoring workflow. All data is stored and in a Postgres database. A visualisation dashboard is used for monitoring model performance (Grafana Labs). 2) We also explored how to use cosine similarity to deliver a verdict on how well an LLM is responding to user queries. A large language model was then used as a secondary evaluation method to guage quality of our user facing model. As always a big thanks to Alexey Grigorev and the team for making these available to many of us who enjoy collaborative learning and hands on projects. https://lnkd.in/drN-v7xv

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