Hopefully, they will eventually open source this, like earlier Qwen models. Qwen2.5-Turbo, which brings the following advancements: - Extended Context - Faster Inference Speed - Lower Costs https://qwen2.org/qwen2-5-turbo/

Hey everyone, It’s exciting to finally be a part of this incredible Data Alchemy community! Data is reshaping every corner of the world, and what excites me most is how this group brings together such diverse minds—from analysts and scientists to engineers and enthusiasts—each solving unique problems in their domains. To kick off meaningful conversations, I’d love to hear more about you: - What’s your current role or project focus in the data space? - What skills or tools are you working on mastering? - And if you could snap your fingers and gain expertise in one area of data, what would it be? For me, it’s always inspiring to see how data professionals are tackling real-world challenges, whether it’s optimizing supply chains, driving AI innovation, or delivering insights that change business decisions. Let’s use this thread to share, connect, and uncover how we can help each other grow. Who knows? We might even find opportunities to collaborate or exchange resources that level up our journeys. Does anyone wish to connect? I am available here https://www.linkedin.com/in/kunal-soni/ Looking forward to learning about your experiences and insights! Cheers, Kunal https://www.mciskills.com/

Hi fellow Data Alchemists, I’m writing here with the goal of studying together the essentials we need to know if we want to become Data Scientists or work with Machine Learning. I’m considering creating a series of posts covering Statistics, Probability, and maybe even some Math needed for Machine Learning. I’m not an expert, so I’ll be gathering information from the web, I'll write the post and I'll ask ChatGPT to correct me. My idea is to create some accountability for myself while sharing my studies with all of you. Today, I thought I’d start with the basics: Measures of Central Tendency. As the title suggests, "Central Tendency" should already give us a hint about what this is about, right? If you're not sure, it’s simply a fancy term for describing the mean, median, and mode. So, what are Measures of Central Tendency? They’re key statistical tools that help us summarize and understand the central point of a dataset. These measures are especially useful in data science for interpreting data distributions and providing meaningful insights into the general behavior of the data. The three primary measures, mean, median, and mode, each give us a unique perspective on this “center.” - Mean: The mean is calculated by summing up all data points and dividing by the count of those points. It’s useful when data is symmetrically distributed, as it represents the expected value. However, it’s sensitive to outliers, so in skewed distributions, it might not accurately represent the center of the data. - Median: The median, or the middle value when data is ordered, is especially valuable in skewed distributions or when there are outliers. Since it reflects positional rather than magnitude-based centrality, it often provides a more robust measure of central tendency than the mean in non-normal distributions. - Mode: The mode, or the most frequently occurring value, is useful in categorical data or multimodal distributions. It offers insights into the most common category or value in the dataset, which can be particularly important for understanding customer preferences, product popularity, or common patterns in discrete data.

https://api-docs.deepseek.com/news/news1120 A run time compute "thinking" model like o1-preview 🔍 o1-preview-level performance on AIME & MATH benchmarks. 💡 Transparent thought process in real-time. 🛠️ Open-source models & API coming soon! 🌐 Try it now at http://chat.deepseek.com

This tutorial demonstrates how to create a SQL agent using Cohere and LangChain. The agent can translate natural language queries coming from users into SQL, and execute them against a database. This powerful combination allows for intuitive interaction with databases without requiring direct SQL knowledge. https://docs.cohere.com/page/sql-agent-cohere-langchain