Carlos O.’s Post

In ML, a smart decision tree can make very good predictions. It often turns out, however, that a collection of less intelligent trees, which cast votes as to the best prediction, will collectively outperform a single, smarter tree making predictions on its own.

🚀 Excited to share our comprehensive guide featuring the best models, tools, and methods for Object Tracking . Our ultimate guide covers top models, tools, and methods for seamless tracking. Read now🏃♀️: https://lnkd.in/dpUjPzTk

🚀 Excited to share our comprehensive guide featuring the best models, tools, and methods for Object Tracking . Our ultimate guide covers top models, tools, and methods for seamless tracking. Read now🏃♀️: https://lnkd.in/djQwP89a

Exploring AI Agents with LangGraph in Automation Hello linkedIn, evolving landscape of incorporation of multi-agent systems into workflows are redefining how businesses approach automation. My latest interest in AI agents have led me to LangGraph to fill my curiosity. My recent Medium post delves deep into the capabilities of LangGraph, a revolutionary framework that leverages graph-based structures to streamline complex workflows. Why LangGraph Matters LangGraph stands out with its graph-based architecture, which models tasks and dependencies intuitively. This not only simplifies the development process but also enhances scalability and efficiency. By integrating seamlessly with popular Python libraries like TensorFlow, PyTorch, and scikit-learn, LangGraph offers a versatile and robust solution for developing advanced AI applications. Key Python Libraries in LangGraph 🔹 TensorFlow: For building and training machine learning models. 🔹 PyTorch: Known for its dynamic computation graph, it’s ideal for research and development. 🔹 scikit-learn: Essential for data preprocessing and traditional machine learning algorithms. LangGraph’s ability to manage extensive data processing and model training tasks makes it invaluable for businesses aiming to harness the power of AI. Whether it’s for automating customer service with AI-driven chatbots, optimizing supply chain logistics, or enhancing predictive maintenance in manufacturing, LangGraph provides the tools necessary for creating sophisticated AI solutions. Impact on Business Structures The integration of AI agents through LangGraph can transform business operations by: Boosting Efficiency: Automating repetitive tasks allows human resources to focus on strategic initiatives. Enhancing Decision-Making: AI-driven insights enable data-backed decisions, reducing risks and improving outcomes. Scalability: LangGraph's modular design ensures that businesses can scale their AI applications as needed without significant overhauls. The Future is Here As we continue to explore the potential of AI in business automation, frameworks like LangGraph are leading the way. By providing a robust, scalable, and efficient platform for developing AI solutions, LangGraph is not just a tool but a catalyst for innovation. 🚀 Ready to revolutionize your business with AI? Dive into my Medium post to learn more about LangGraph and how it can transform your operations. Let's embrace the future of automation together! Check out my medium post with full guideline for implementation. https://lnkd.in/dp_7mWnK #LangChain #Automation #LangGraph #MachineLearning Feel free to reach out if you have any questions or need further insights. Let's shape the future of automation, one graph at a time! 🌟

Fine-tuning models, a topic that consistently ignites fascination and complexity in the world of AI and ML.   Just recently, Almost Timely News dissected this concept, providing an insightful exploration that I couldn't agree more. In this detailed piece, the author delves into the intricacies of fine-tuning models, shedding light on the nuances that can often feel like navigating a labyrinth for those in the field.    The article provides not just an explanation, but a guide, a map through the often convoluted world of fine-tuning models. It's an exploration that I believe every AI enthusiast, every entrepreneur in the tech space, should embark on.   Feel free to read it here: [Almost Timely News, August 27, 2023: A Deep Dive into Fine Tuning Models](https://lnkd.in/e9MvZFrz) #ai #ml #machinelearning #largelanguagemodels #llm #finetuning #aiml #convolutionalneuralnetworks #nlp #huggingface #llama2 #gpt4 #openai #generatieveai #genai

Want to understand how LLMs and Transformers work? This is the best visualization. The article makes you understand the fundamental concepts in 5 min.

A Play for today, as again, it's Thursday 💡 Type in a few ideas you would like to brainstorm. Think Machine will create an interactive 3D graph for you (AI knowledge graph). You can also change how different ideas connect. Enjoy 😎 #thursdayfun #AImindmap #mindmap

Interesting paper on what happens when you train generative models on it's own outputs. https://lnkd.in/gHfXu85Y

Revolutionize Your Vision with AGImageAI 👁️🗨️ Imagine a world where your visual data becomes a source of untapped insights. AGImageAI, our revolutionary image analysis tool, propels your organization into the future. Harness the power of cutting-edge computer vision and deep learning to transform everything from quality control to security, and step through the gateway to transcendent perception. 🔍

Lately I’ve been reading 𝐃𝐞𝐬𝐢𝐠𝐧𝐢𝐧𝐠 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐒𝐲𝐬𝐭𝐞𝐦𝐬, by Chip Huyen (https://lnkd.in/dgfXim8H). It’s proving to be a great resource for ML in the real world. One of my best takeaways so far has been having a clear definition of many of the good practices I have learned along the way from a myriad of heterogeneous sources and my own experience. Having a robust knowledge base I can refer to is a great tool for my work. For example, did you know that checking the performance of your ML model in the relevant subsets of your data (instead of globally) is called 𝐬𝐥𝐢𝐜𝐞-𝐛𝐚𝐬𝐞𝐝 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧? I you have ever dabbled with micro-, macro- and weighted- metrics and tried to avoid Simpson’s paradox, you know what I am talking about. You can even do this per-instance, such as we did at MOONVISION five years ago, where we evaluated the performance of our object detection models on a per-image basis: https://lnkd.in/d5dhxywz. The book offers many more nuggets of valuable insights for ML in production, so just go and read it!