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

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.

Just completed by Python course on Kaggle https://www.kaggle.com/learn/certification/kirtisinghchd/python

Are you seeking a way to prosper and provide for your family? Do you desire financial freedom and flexibility? God has blessed us with talents and skills to steward, and entrepreneurship is a powerful way to honor Him. Dropshipping, in particular, offers an accessible path to starting a business, whether as a side hustle or main venture. With minimal upfront costs and scalable growth potential, dropshipping allows you to trust God's provision while taking practical steps towards financial stability. As Proverbs 22:29 says, 'The diligent will rule, while the slothful will be put to forced labor.' By faith, take the first step and explore dropshipping as a means to bless your family, community, and ultimately, God's kingdom. Remember, 'Whatever you do, do it all for the glory of God' (1 Corinthians 10:31). Start your dropshipping journey today and watch God's blessings unfold! You can join my Dropshipping community to get started as well: https://chat.whatsapp.com/HxzqApfnwQLAPpnxsikJrZ