Carlos Daniel Jimenez

Graph-based Euclidean clustering is reshaping point cloud segmentation by automating processes, reducing noise, and offering robust scalability for complex scenes. Dive deeper in this article by Dr. Florent POUX. #MachineLearning #Clustering

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A Gentle Introduction to XGBoost Loss Functions https://lnkd.in/gfi3c7n

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From our AI CTO, Miguel Amigot II: 🚀 By leveraging Prompt Adaptation, LLM Approximation, and LLM Cascade, ibl.ai optimizes AI performance while cutting costs. 🎯 Precise prompts minimize computational load, fine-tuned smaller models handle specialized tasks, and our tiered querying system ensures efficient resource use. 🌿 Transcript: At ibl.ai, we harness the power of AI efficiently and responsibly. Let’s explore three advanced techniques we use to optimize costs while enhancing the capabilities of large language models. Prompt Adaptation is our first technique. By crafting concise, optimized prompts, we minimize computational load. For example, instead of using lengthy few-shot prompts with multiple examples, we select the most effective ones. This not only reduces processing costs but also enhances the model's focus and effectiveness. Let’s consider email classification. We transform a bulky prompt into a lean one by selecting the top five most relevant past examples using semantic similarity tests. This approach reduces token count significantly, slashing costs by up to 70%. Next is LLM Approximation. Rather than defaulting to high-cost models for all tasks, we employ a strategic use of cached responses and fine-tune smaller models for specific functions. This method maintains high accuracy but with lower operational costs. For instance, when recurrent queries or similar tasks arise, we retrieve answers from a cache, which saves up to 95% in costs. Additionally, by fine-tuning smaller models on specialized tasks, we achieve or surpass the performance of larger models without the associated expense. The third technique is LLM Cascade. We start with the simplest model that might provide a satisfactory answer and escalate to more complex models only if necessary. This approach ensures we use the minimal computational resources required for each query. Using an email sorting task, we first query a basic model. If the confidence score is sufficient, we stop there. If not, the query moves up to more sophisticated models. This tiered querying significantly cuts costs while ensuring quality responses. At ibl.ai, these techniques—Prompt Adaptation, LLM Approximation, and LLM Cascade—are more than just cost-saving measures. They are part of our commitment to sustainable AI use, ensuring that our technologies not only lead in innovation but also in efficiency and responsibility.

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Using Normalization Layers to Improve Deep Learning Models https://zurl.co/LW2h

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🚀 Just read an amazing new blog post by Tanel Poder on visualizing and comparing embedding vectors as heat maps! 🔥 If you're into data visualization or machine learning, this is a must-read. Tanel breaks down complex concepts into easy-to-understand visuals. How do you currently visualize your data? Do you think heat maps could improve your workflow? Let's discuss! 👇 #DataScience #MachineLearning #DataVisualization

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Get ready to optimize your NLP models with vLLM! In his latest blog post, Sergio Morales Esquivel explores the practicalities of streamlining NLP models for real-world efficiency. In it, you'll learn: ✅​ About the vLLM library for efficient model serving ✅​ Practical strategies for conserving computing resources during inference ✅​ The art of batched inference for seamless AI deployments Read on and discover how to make your models perform smarter, not harder: https://lnkd.in/egnUbWsk #Data #DataEngineering #DataScience #ArtificialIntelligence #NLP

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5 Free Resources for Understanding Neural Networks https://zurl.co/TP6F

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Quantization of Convolutional Neural Networks: Quantization Analysis

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5 Free Resources for Understanding Neural Networks https://zurl.co/4WtL

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A survey on MultiModal Large Language Models covers: - cost-effective training strategies - comprehensive model architecture and training pipeline overview - performance evaluation on mainstream benchmarks It's a deep dive into 26 cutting-edge models https://lnkd.in/esjakMbz

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Understanding Gradient Boosting for Classification: A Practical Approach via #TowardsAI → https://bit.ly/3XWpe5h

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Poor Pablo. He expected a fast-paced environment, not a test of patience. Here’s some light reading to do while you wait. Learn about ML with ridge and lasso regression with our course notes 👉https://bit.ly/44zYdaN . . . #datascience #machinelearning #mltraining #PoorPablo #geekhumor

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The second episode of Hillside PatternCast is online! https://lnkd.in/dP4piJWn In this episode, Eduardo Guerra and Hironori Washizaki, talk about important patterns that can be applied when designing a software architecture with components that use Machine Learning.

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Learn how to build a neural network in TensorFlow — just follow along Shreya Rao's step-by-step guide (with a bonus section for anyone interested in a PyTorch implementation as well).

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Learn how to build a neural network in TensorFlow — just follow along Shreya Rao's step-by-step guide (with a bonus section for anyone interested in a PyTorch implementation as well).

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