Varun Grover’s Post

A series of benchmarks show the value of GraphRAG for question & answering on complex documents Lettria's evaluation emulated hundreds of questions based on a very comprehensive framework: - Factoid questions - Multi-hop questions - Numerical Reasoning questions - Tabular Reasoning questions - Temporal Reasoning questions - Multiple Constraints questions Their results indicate a GraphRAG solution boosts Q&A performance by more than 35% on average, compared to the default RAG configuration Link in comments. At #CDL24, we have a well-rounded program that explores different aspects of #KnowledgeGraphs #GraphDB Graph #AI #DataScience #MachineLearning #SemTech #EmergingTech #GenAI #LLM #RAG. We even have a dedicated Gen AI and RAG Stage - check it out! https://lnkd.in/dmrRpV4c

Handling Imbalanced Datasets in Machine Learning Imbalanced datasets can significantly impact machine learning models, especially when the minority class holds critical insights. To address this, two popular techniques come into play: Upsampling: Increasing the representation of the minority class by duplicating or synthesizing data points, helping the model learn more about this class. Downsampling: Reducing the size of the majority class to match the minority class, ensuring a balanced dataset without overloading the model. Each method has its pros and cons, and the choice depends on the specific problem you're solving. A balanced dataset leads to better recall, precision, and overall model fairness. git- https://lnkd.in/deyundDY #MachineLearning #DataScience #AI #ImbalancedData #Upsampling #Downsampling

Great News! 🎉 We are excited to introduce the new section of our journal, "Optimization, Big Data, and AI/ML"! This section publishes advanced theoretical studies and practical applications on topics related to optimization, machine learning, and big data involving the use of fractional calculus (FC), the concept of fractal, as well as general fractional-order thinking (FOT). For more information on this section, please visit the link below: https://brnw.ch/21wJcU5 #newsection #bigdata #AI #ML #fractionalcalculus #FOT

How does a machine learning algorithm compare data from different sources having different units? The process is converting Normal/Gaussian Distribution to Standard Normal Distribution which is called 𝘚𝘵𝘢𝘯𝘥𝘢𝘳𝘥𝘪𝘻𝘢𝘵𝘪𝘰𝘯 of data. Standard Normal Distribution is a Gaussian Distribution having mean μ = 0 and standard deviation σ = 1. This can be achieved by 𝘡-𝘴𝘤𝘰𝘳𝘦. Z-score = (Xi-μ)/σ In this way each Feature of a Dataset can be standardized, can be compared with others and ML models can be trained efficiently. #MachineLearning #ArtificialIntelligence #DataScience #DataAnalysis #AI

I've noticed many practitioners often confuse Quantization with Downcasting - two fundamentally different concepts in ML optimization. 🔍 Let's clarify: Downcasting: Simply converts data to a lower precision format (float32 → float16) Preserves numerical representation, just with less precision Quantization: Transforms data into an entirely different numerical space Maps values to a new representation system with careful consideration of information preservation 📚 Want to dive deeper? I highly recommend this course on Model Quantization: https://lnkd.in/gjcByXP7 Special thanks to DeepLearning.AI and Hugging Face for collaborating on this excellent resource. It really helped deepen my understanding of quantization techniques! #MachineLearning #Quantization #DeepLearning #AI #ModelOptimization

#Day140ofDeepLearning: 🎯Tried Learning Attention Mechanism in Sequence to Sequence learning. While didn't completed understood the workings but what i understood are: - Attention Mechanism helps to show the role to each timestamp of encoder in prediction of decoder. - For attention based Encoder-Decoder, we need to provide [Yi-1, Si-1, Ci], where Ci is attention i/p calculated using ANN. - It stabilizes BLEU(Bilingual Evaluation UnderStudy) Score which helps to measures the quality of machine translated text. - Use of ANN while calculating weighted sum helps to plot Weights which helps to understand the impact of hidden states on each predictions. Github: https://lnkd.in/dDXu6rid #machinelearning #ai #deeplearning

🔍 Understanding Supervised vs. Unsupervised Machine Learning 📊 Supervised machine learning involves training models to understand the relationship between features and known labels from historical data, enabling them to predict labels for future cases. This includes: Regression: Predicting numeric values. Classification: Categorizing instances into predefined classes. In binary classification, models determine if an instance belongs to a specific class or not. Multiclass classification expands this to predict one of several mutually exclusive classes. On the flip side, unsupervised machine learning focuses on finding patterns and similarities within data without predefined labels, grouping observations into clusters based on their inherent characteristics. Understanding these concepts is crucial for leveraging data to drive insights and decisions. #MachineLearning #DataScience #AI #Analytics #artificialintelligence #ML

logistic regression is a powerful and versatile algorithm that serves as a solid foundation for many machine learning tasks. Its combination of simplicity, efficiency, and interpretability continues to make it a popular choice for practitioners and researchers alike. Embrace the power of logistic regression to enhance your predictive modeling capabilities! #LogisticRegression #MachineLearning #AI #DataScience #BinaryClassification

Recently, I used Anthropic Claude 3.5 Sonnet to generate synthetic data with a minimal prompt (of course, with some examples). The results are pretty good, and when questioned about its methodology (type of instructions or approach), the model attributed its performance to its training without referencing any specific system prompt. In contrast, GPT-4o struggled with similar tasks, often providing repetitive responses that failed to grasp the context. Claude is figuring out my context/intention without explicit prompting! #ai #llms #generativeai #graphs #knowledgegraphs #syntheticdata