Rasmus Just

"Mastering SVM: A Guide to Different Kernel Functions 🧠✨ Support Vector Machines (SVM) are versatile tools in the machine learning toolkit, but understanding how to choose the right kernel function is key to unlocking their full potential 🔓. Here’s a deeper dive into the most popular SVM kernels: 📈 Linear Kernel: Best for when your data can be separated with a straight line 📊. This kernel is simple and efficient, making it perfect for high-dimensional datasets where a clear boundary exists 🔒. 📊 Polynomial Kernel: This kernel can capture more complex relationships in your data by considering polynomial combinations of features 🔍. It’s ideal when your data isn't linearly separable but still follows some predictable pattern 📈. ⚡️ Radial Basis Function (RBF) Kernel: Known as the Gaussian kernel, it's the most flexible choice 🤯. The RBF kernel can handle complex, non-linear relationships by mapping the data into a higher-dimensional space, making it powerful for real-world, messy datasets 🌐. Choosing the right kernel function is crucial to maximizing your SVM model’s accuracy and effectiveness 📊. Whether you need simplicity or the ability to model complex data relationships, there’s a kernel function for every need 🎯! Dive deeper into SVMs and elevate your machine learning game today! 🚀 #MachineLearning #SVM #KernelFunctions #DataScience #ArtificialIntelligence #TechTalk #DataAnalytics #AI 🤖"#SkillzRevo 💻 #NorthAmerica 🇺🇸 #USA 🇺🇸 #Dubai 🇦🇪 #MiddleEast 🌍"

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👉 Learn how to simulate pre-trained models from #PyTorch and #TensorFlow directly in #Simulink with the new co-execution Simulink blocks. https://spr.ly/6044W3drs

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This week's Tuesday Tutorial takes our exploration of neural networks to the next level by applying them to Universal Differential Equations: https://lnkd.in/gbyZfBUU. In this installment, we generate data and train a neural net to partially learn a Lotka-Voltera system using Collimator. #controlsystems #controlsengineering #controltheory #smartsystems #engineering #python #JAX #collimator #simulation #technology #machinelearning #ml #ai

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Not long ago, we discussed ways to speed up differential expression analysis in single-cell datasets. In our new blog post, we discuss 'presto', an R package to make single-cell differential expression analysis faster: https://lnkd.in/dRS-mxJi #SingleCell #Transcriptomics #DifferentialExpression #Lexanomics

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🚀 Our team at Allometrik just turned an exciting paper on AgentTorch into an automated podcast video! 🎥🤖 This paper introduces a groundbreaking framework that scales Agent-Based Modeling (ABM) to millions of agents using large language models (LLMs). This innovation pushes the boundaries of simulation, offering real-world adaptive behavior at an unprecedented scale, and it's open-source! 🌍 Using New York City’s COVID-19 outbreak as a case study, AgentTorch simulates 8.4 million agents and predicts health and economic outcomes with incredible accuracy. The potential here is game-changing, not just for policy-making but also for advancing scientific discovery. If you're into AI, simulations, or decision-making models, this is something to keep an eye on. 👀🔥 Check it out below! 📺 👉 Want to know how we can apply agentic systems your business? Drop us a DM or chat with us today! #AI #AgentTorch #YouTube #Automation #ABM #LLM #AIForGood #DataScience #OpenSource #MachineLearning #Innovation #Agents

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Let's evaluate Llama3's RAG performance on an arXiv paper (about RAG evaluation, this is a very meta notebook, puns intended)! In this tutorial, we build on our work earlier this week in combining Unstructured's API for pdf document preprocessing with OpenAI's GPT-4o + ragas for evaluation, Hugging Face for Llama3, and LangChain to integrate all of these systems to evaluate Llama3. Llama3's metrics on RAG for our quick example: context precision: 0.9867, faithfulness: 0.8297, answer relevancy: 0.8643, context_recall: 0.9733 Try this out with your favorite unstructured data by swapping the arXiv pdf URL in the notebook! Colab Notebook: https://lnkd.in/gSndzTuC

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OpenAI's new o1 model outperforms every single model on the Norway Mensa IQ test. It scored 25 out of 35, equivalent to an IQ of 120. The model demonstrated strong pattern recognition and reasoning skills, solving complex visual-spatial problems that most humans find difficult. Unlike previous models, o1 uses advanced reasoning algorithms that allow it to detect underlying patterns in data, pushing AI closer to human-level intelligence in specific cognitive tasks. What does this mean for the future of LLMs? Full analysis: https://lnkd.in/ga8bqGrT ↓ Are you technical? Check out https://AlphaSignal.ai to get a daily summary of breakthrough models, repos and papers in AI. Read by 200,000+ devs.

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