paradime.io’s Post

New features ✨ are here, including our new Anthropic integration! ➡️ Work with #Claude in prompt and data workflows ➡️ Organize prompt outputs with custom delimiters ➡️ Easily upload data in CSV format ➡️ Experiment with the limited release of batch actions to annotate data at scale. We're excited to see what you build in June! #JuneReleases #DataEngineering #PromptEngineering #LLMs #Anthropic

Did you ever wonder if you could select only certain people from your life and filter the rest of them with AI, well you can't but you can use AI to select data with SQL. Yes !! With LlamaIndex libraries we can convert text to sql queries and create a RAG pipeline with a relational database . In this video we will use Llama3 model with Groq to do Text to SQL in just 15 mins. Jerry Liu Thank you for this amazing feature. Colab notebook - https://lnkd.in/ewEtszWb #llamaindex #llama3 #rag #groq #sql #texttosql #relationaldatabase #rds https://lnkd.in/e4yZS7sa

Curious about converting any text data into a Knowledge Graph? 👉 Get the answer here: https://lnkd.in/gmMqki6R Throughout the week, I've been exploring the most effective methods to achieve this. Starting with Leann Chen’s insightful tutorial on leveraging spacy-llm to extract entities and relationships, and also inspired by Milena Trajanoska’s innovative approach using schema.org (https://lnkd.in/gNQj-zrT). In the end, I discovered that utilizing LLAMA3 & Groq for converting text into a knowledge graph is the most efficient method. Best part? It's entirely free, and I'm genuinely impressed with the results. Feel free to experiment with my code for your own data and use cases. Last but not least, after you’ve watched my tutorial, you might be wondering if there's a solution that wraps it all up neatly. Enter LLMGraphTransformer, a remarkable creation by Tomaz Bratanic (https://lnkd.in/g-Kzngsb). While incredibly useful, it's currently limited to OpenAI and Mistral models. If you've had experience constructing a knowledge graph from unstructured text, I'd love to hear about it! Share your insights in the comments below.

📊 How do you efficiently retrieve large datasets through your APIs? Tomorrow, we’re diving into the Retrieval Operation pattern—perfect for handling large read-only requests like reports and data lookups. Want to know how to optimize your API’s data retrieval? Stay tuned! 💬 How do you manage data retrieval in your systems? Let’s chat! #APIDesign #TechTalk #SoftwareDevelopment #DataRetrieval #CodingLife #APIs

🚀 Exciting Updates to TidyDensity! 🚀 I'm thrilled to announce some fantastic new features and improvements in the latest update of the TidyDensity package! 📈 What's New? - Negative Binomial Distribution: Calculate AIC with `util_negative_binomial_aic()`. - Zero-Truncated Distributions: Parameter estimation, AIC calculation, and summary tables for Negative Binomial, Poisson, Geometric, and Binomial distributions. - F Distribution: New functions for parameter estimation and AIC calculation. - Pareto, Paralogistic, and Inverse Distributions: Enhanced support with new parameter estimation and summary table functions. - Generalized Distributions: Expanded capabilities for Gamma and Pareto distributions. Minor Improvements - Optimized Parameter Estimation: `util_negative_binomial_param_estimate()` now uses `optim()` for better accuracy. - Improved Data Handling: `quantile_normalize()` now includes column names for clearer data presentation. These updates significantly enhance the analytical capabilities of TidyDensity, providing more robust tools for distribution analysis. Whether you're dealing with standard or specialized distributions, these new features will streamline your workflow and improve your results. Don't miss out on these powerful new tools—update your TidyDensity package today and take your data analysis to the next level! 📰 News: https://lnkd.in/ea7mX_Xg Happy coding! 💻 #Rstats #DataScience #TidyDensity #Analytics #DataAnalysis #RProgramming #Update #Coding #Statistics #StatisticalDistributions Anna Anisin Hadi Heidari Mehdi Gorji nia Khalili Margarita S. David Langer David Kun Jake Waddle Jake Riley Joachim Schork Darko Medin Olga Palkovskaya Veerle van Leemput 🔥 Matt Dancho 🔥

SheetChat is a tool I have created to make data science easier. It is like a conversation, and you get information from data. #datascience #statistics #openai #chatGPT #dataanalysis https://lnkd.in/dqaNyX3Y

Whichever format your dataset is in, KNIME Analytics Platform probably has you covered. A few weeks ago I was working on JSON files (which I had retrieved using via API, all in KNIME). This week for the #JustKnimeIt challenge, the metadata for the dataset was in XML format. Using the XPath node, you can extract the relevant information from the different nodes in the file. For example, given the code of a group of diseases in the main dataset, one can get all the descriptions of the diseases (from the XML metadata file) and then use a Value Lookup node to add the (human reader friendly) descriptions into the dataset. So XML is added to the list of data formats I have worked with in KNIME. Which other not so popular format do you know, and I am sure KNIME has a node for it..

Discover the power of Seaborn for your data visualizations! 🤠 With beautiful default styles🤩, built-in statistical functions, seamless Pandas integration, concise syntax, and strong community support, Seaborn makes creating stunning plots a breeze. Say goodbye to the struggles of matplotlib 🫸and hello to effortless visualization with Seaborn👋.✌️ #Seaborn, @datacamp

Why is KNIME considered a no-code/low-code tool? This is because, in addition to providing various conveniences in data analysis that do not require any coding, there are certain situations where we still need to do low coding. For example, in my work on [L1-AP] Data Literacy with KNIME Analytics Platform: Basics, I did some light coding to categorize people based on their age. #JustKNIMEIt

A Tidy Release for tidyAML The tidyAML R package just received a tidy update with version 0.0.4! This release includes some nice new features and fixes to help data scientists quickly build predictive models in a tidy framework. What's New? The main highlights of this release include: - Add the `extract_regression_residuals()` function (#187, #198) to easily extract residuals from regression models. This is handy for diagnostics and plotting. - New `.drop_na` parameter for `fast_classification()` and `fast_regression()` (#199) to optionally drop observations with missing values when training models. - Expand the core packages preloaded by `core_packages()` (#186) to include useful extras like `discrim`, `mda`, `sda`, and more. - Fixes to `internal_make_wflw_predictions()` (#190) to return all data - the training predictions, testing predictions, and the original data. Installation As always, you can install the latest tidyAML 0.0.4 release from CRAN: install.packages("tidyAML") Or get the development version from GitHub: devtools::install_github("spsanderson/tidyAML") We hope you find this update useful! Let us know if you have any feedback on the package or new features you'd like to see added. Happy modeling! #R #RStats #parsnip #tidymodels #tidyaml #regression #classification #residuals #klar #liquidsvm #discrim #sda See attached. Post in the comments. Hadi Heidari Mehdi Hamedi, MD Margarita S. Alier R. Abdul-Mateen Qamardeen Ridwan Suleiman ADEJUMO Ransingh Satyajit Ray Rami Krispin David Langer David Kun Aya Eljibali Nicola R. Fabiano Araujo Ivo Agbor Arrey