Simon See

💻 Developing a Baseline Machine Learning Model with Project CCHAIN Data: A Case Study Review 🧬 Rowen, our Machine Learning Algorithms Advocate, has crafted an insightful case study review in his latest article. In this piece, Rowen shares a case study review of the development of a baseline machine learning model designed to predict dengue fever outbreaks in Zamboanga City, Philippines. Drawing from 20 years of data provided by Project CCHAIN—including climate, demographics, healthcare infrastructure, and economic activity—this model employs a Random Forest classifier. The study highlights how incorporating lagged features to capture temporal patterns led to high accuracy in predicting dengue outbreaks on both training and testing datasets. This review underscores a significant advancement in public health predictive modeling. Link to the case study review in the comment section ⬇️ #MachineLearning #ProjectCCHAIN #AIPilipinasCebu #AIAdvocateProgram

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Visualizing a simply diagram on how to incorporate #AI into your #SPM system to resolve disputes. Starting with the problem definition of resolving disputes (not avoiding at this point), this diagram shows the starting points and flow of how to white board than expand on your intelligent SPM journey. #Varicent #Xactly #SAP #CIQ #Incentivate

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Semantic Chunking or Fixed-Size Chunking: What’s Best for RAG Systems? Our latest blog by Vectara engineers Renyi Qu and Forrest Sheng Bao dives deep into the debate around chunking strategies in Retrieval-Augmented Generation (#RAG) systems. The results? Eye-opening insights into performance, practicality, and computational efficiency. 🔑 What You’ll Learn: ✅ Why fixed-size chunking excels in real-world applications. ✅ The hidden costs of semantic chunking—and when it’s worth it. ✅ How embedding quality can drive greater RAG success. Curious to know more? Read the full blog TODAY! 👉 https://bit.ly/3Z7f56j

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How do you overcome issues caused by availability of data ❓ 🎤 Yajuan Wang - Director of Machine Learning - Teladoc Health is on stage with the keynote presentation, 'How Can You Successfully Scale Up AI for Business Value?'. #CDAOSpring

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Find Sajjadur Rahman (hiring) at the #GuideAI workshop a #SIGMOD2024, presenting #CMDBench, a benchmark designed to model the complex landscape of enterprise data platforms. By adapting existing datasets, CMDBench evaluates coarse- and fine-grained data discovery and task execution. Join Sajjadur for his presentation at 3 PM CST during Session 3. https://lnkd.in/g3ETpFME #MachineLearning #AI #LLMs #Data #DataScience #AI

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Attractor networks are building blocks of many dynamic neural networks. The #architecture proposed in this paper is a step towards overcoming the von Neumann bottleneck by accelerating matrix-vector multiplication in #Resistive #memory #devices #nonvolatile #neuralnetworks #AI

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I'll be attending the IEEE Conference on Artificial Intelligence (CAI) on the sunny island of Singapore next week! If you're also planning to be there, I'd love to connect and discuss the latest AI advancements. 2024 IEEE Conference on Artificial Intelligence https://ieeecai.org/2024/ #AIforgood #responsibleAI

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Building a Retrieval Augmented Generation (RAG) app? Take a look at Alexander Gilmore's new article, in which he explores a few alternative graph data models that can be used to enhance these applications and the unique benefits each model provides. Discover how to build a more efficient & powerful app! https://bit.ly/3VTPfCY #RAG #LLms #Neo4j #graphdatabase

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Best Practice Guide - Deep Learning ↳ Explore essential considerations for choosing between CPU and GPU architectures in deep learning, helping you optimize performance for your projects. ↳ Understand the significance of I/O for large-scale distributed deep learning, ensuring efficient data handling and processing. ↳ Gain insights into performance metrics for different CPU and GPU architectures, guiding you in selecting the best hardware for your deep learning tasks. ↳ Learn the fundamentals of artificial neural networks and deep learning algorithms, providing a solid foundation for your deep learning journey. Ready to elevate your deep learning skills? Dive into our comprehensive guide and unlock the full potential of deep learning technology! ✅ Get any Data science training videos, https://lnkd.in/gQVwVNSG ✅ Subscribe to our Youtube channel: https://lnkd.in/gD54ZjUh ✅ P.S. Want to Upskill your Data Science workforce? Check out our course catalog for corporate training, https://lnkd.in/dYipv_Qm #datascientists #datascience #machinelearning #algorithms #artificialintelligence #python #community #training #tableau #sql #jobopportunities #success #job #chatgpt4

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Quantization is vital for managing the vast computational demands of deploying large language models (LLMs). Traditional methods face performance and accuracy challenges. Enter the Quattuor-Octo-Quattuor (QoQ) algorithm and QServe system, innovations from MIT, NVIDIA, UMass Amherst, and MIT-IBM Watson AI Lab. These advancements drastically improve throughput and efficiency. Discover more in our latest blog - https://lnkd.in/g_GnaxgQ 😍 Follow AI Toolhouse for more such amazing content. 🌟 Explore 𝟑𝟔𝟎𝟎+ latest AI Tools here for FREE ➡️ https://lnkd.in/dpQB7xZU #AI #MachineLearning #Quantization #LLMs #TechInnovation #NVIDIA #MIT #AIResearch

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Explore the evolving field of long context retrieval in Large Language Models (LLMs) and learn how techniques like Retrieval-Augmented Generation (RAG) optimize LLM performance. This detailed article dives into the technical workings, challenges, and hybrid strategies that combine extended context windows with retrieval mechanisms, enhancing efficiency for complex tasks like summarization, question-answering, and legal research. Read the full article here: https://lnkd.in/gbXw2nxZ #largelanguagemodels #rag

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Another snippet from our recent webinar with #Complytek CEO Faisal I. and SME Alia Mahmud... Supervised learning is one of the most commonly used techniques in machine learning, where the model is trained on labeled data. In this approach, the algorithm learns from input-output pairs, with the goal of mapping input data to the correct output. Supervised learning is a powerful and widely used approach in machine learning, where the model learns from labeled data to predict outputs for new, unseen inputs. Its success hinges on the availability of high-quality labeled data and the appropriate selection of algorithms and hyperparameters. It has numerous applications in fields like image classification, financial forecasting, medical diagnosis, and more. Despite its advantages, challenges like overfitting, data labeling, and data imbalance must be carefully managed to ensure optimal performance. #BankingCompliance #AI #MachineLearning #GCIPrograms #Complytek #SupervisedLearning #BankingTraining...

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⛱️ Mixture of Experts Primer 🚀 The Mixture of Experts (MoE) model is revolutionizing large language models (LLMs) by enabling unprecedented scalability and efficiency. 🌟 🔗 Link to the primer on vinija.ai https://lnkd.in/gTS7eBzC . Written in collaboration with Aman Chadha MoE divides complex problems into smaller sub-tasks, each managed by specialized sub-models or "experts." This not only enhances performance but also optimizes computational resources by activating only the most relevant experts for each input. Key aspects: - Scalability: Linear increase in capacity without added computational complexity. - Efficiency: Activates only the necessary experts, reducing computation. - Flexibility: Adapts to various tasks with specialized experts. MoE is the driving force behind some of the most advanced AI models today: - Google’s Switch Transformer - OpenAI’s GPT-4 - Mistral AI’s Mixtral - Google’s Gemini 1.5 These advancements highlight MoE’s potential to create more powerful and efficient AI solutions. 🌍

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The paper "Scalable MatMul-free Language Modeling" by Rui-Jie Zhu et al. introduces a method to eliminate matrix multiplication (MatMul) operations in large language models (LLMs), significantly reducing memory usage and computational cost. Their MatMul-free models perform comparably to state-of-the-art transformers, scaling effectively up to 2.7 billion parameters. They achieved memory reductions of up to 61% during training and over 10x during inference by using a custom FPGA hardware solution, which highlights the efficiency of lightweight operations for future language model accelerators. For further details, you can read the full paper https://lnkd.in/eZidqJuk

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