Sumit Kumar’s Post

TASK NO4:visual question answering I have utilized a visual question answering (VQA) model by leveraging Google Teachable Machine, an innovative platform that simplifies machine learning model creation. The model is designed to interpret and respond to queries about images, merging the capabilities of computer vision and natural language processing. With Teachable Machine, I was able to train the VQA model by uploading diverse sets of labeled images and corresponding questions and answers. This straightforward interface allowed for rapid iteration and fine-tuning of the model to improve accuracy and reliability. By harnessing Google's advanced algorithms, the model can effectively understand the context of an image and provide relevant answers to questions posed in natural language. For instance, it can identify objects within an image, describe scenes, and infer relationships between different elements. This process involves extracting visual features from the image and correlating them with the semantic meaning of the question to generate an appropriate response. #AIMERS #AIMERSOCIETY #APSCHE #Loksabha #2024Results #PowerBI #DataVisualization #DataAnalytics #BusinessIntelligence #DataScience #BigData #DataDriven #DashboardDesign #BI #DataInsights #PowerBICommunity #DataStorytelling #Visualization #Analytics #DataReporting

New blog post alert! 📚🔍 Just published: "Enhancing RAG with Vector Stores: Optimizing Metadata for PDF and DOCX Files" Discover how to: - Optimally store unstructured PDF and Word DOCX in vector stores - Leverage document metadata in RAG systems - Build smarter, context-aware AI document analysis - Implement precise retrieval techniques Read here: https://lnkd.in/gWb7AvCx Your thoughts? Let's discuss in the comments! #VectorDatabases #MachineLearning #AI #GenerativeAI #Langchain

📚 TF-IDF: The Power of Term Frequency-Inverse Document Frequency 📚 In the realm of natural language processing and information retrieval, TF-IDF (Term Frequency-Inverse Document Frequency) is a statistical measure that evaluates how relevant a word is to a document in a collection of documents. It's a cornerstone of many text analysis and machine learning algorithms, helping to transform raw text data into a format that can be easily understood and processed by algorithms. 🔍Understanding TF-IDF - Term Frequency (TF): This measures the frequency of a word in a document. If a word appears frequently in a document, its term frequency is high. - Inverse Document Frequency (IDF): This measures the importance of a word across a set of documents. If a word appears in many documents, its inverse document frequency is low, indicating it's a common word. If it appears in few documents, its IDF is high, indicating it's a unique or important word. The TF-IDF value for a word in a document is calculated as the product of its term frequency and its inverse document frequency. This value helps to weigh the importance of a word in the document relative to the entire corpus of documents. 💡Why TF-IDF Matters - Feature Extraction: TF-IDF is used to convert text data into a set of numerical features that can be used as input for machine learning algorithms. - Document Similarity: It helps in measuring the similarity between documents, which is crucial for tasks like document clustering, search engines, and recommendation systems. - Text Classification: By transforming text into a numerical form, TF-IDF enables algorithms to classify documents based on their content. 🚀 Applications of TF-IDF - Information Retrieval: It's used in search engines to rank documents based on their relevance to a search query. - Text Mining: It's a fundamental step in extracting meaningful information from text data for various applications, including sentiment analysis, topic modeling, and more. Machine Learning: It's used as a feature in many machine learning models to improve their performance on text-based tasks. 📈 Conclusion TF-IDF is a powerful tool in the toolkit of data scientists and machine learning engineers working with text data. By understanding and applying TF-IDF, we can unlock the potential of text data, enabling more accurate and insightful analyses. #AI #MachineLearning #DataScience #NaturalLanguageProcessing #TFIDF #TextAnalysis

Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make Your LLMs Use External Data More Wisely The research paper provides an in-depth exploration of how large language models (LLMs) can be enhanced by integrating external data through techniques like Retrieval-Augmented Generation (RAG). The paper offers solutions like multi-step RAG, tree-based retrieval structures, and iterative retrieval techniques to enhance the accuracy and relevance of responses. It also explores three approaches to integrating external data: using the data as input context, creating smaller models trained on specific data to guide retrieval, and fine-tuning LLMs to become domain-expert models. Key Takeaways: 🔹RAG Methodology: This integrates LLMs with external databases or documents during the response generation process. It highlights techniques such as multi-modal document parsing, chunking strategies, and hybrid retrieval methods to improve the model's understanding of both structured and unstructured data. 🔹Task Categorization: The survey classifies user queries based on their complexity and the required interaction with external data, allowing for targeted approaches to improve LLM performance across various industries. 🔹 Future Directions: The paper suggests that advances in multi-agent workflows, in-context learning, and fine-tuning will further enhance the ability of LLMs to solve complex tasks using external data. The paper concludes that there is no one-size-fits-all solution for RAG or data-augmented LLM applications. Depending on the complexity of the task, different methods such as prompt tuning, fine-tuning, multi-step retrieval, and hybrid retrieval strategies are necessary. The authors highlight the importance of correctly classifying the type of query and using appropriate techniques to balance accuracy, scalability, and computational efficiency. #GenAI #AI #LLM #RAG #datascience #Analytics #NLP #finetuning #AIAgents Reference :https://lnkd.in/gU2UhYF3

Stanford University researchers developed Recursive Abstractive Processing for Tree-Organized Retrieval (RAPTOR), a system that improves context retrieval for large language models. Using a recursive cycle of summarizing, embedding, and clustering, RAPTOR condenses long passages into shorter, relevant excerpts, enhancing retrieval of key details even with limited input lengths. Read our summary of the paper in #TheBatch: https://hubs.la/Q02zvCSl0

Large Language Model (LLM) of Business The Large Language Model (LLM) of Business is a cutting-edge technology that utilizes artificial intelligence and machine learning algorithms to analyze vast amounts of data in order to assist in decision-making processes within organizations. This model is capable of processing large volumes of unstructured data from various sources, such as financial reports, customer feedback, market trends, and social media interactions. By leveraging natural language processing capabilities, the LLM can extract valuable insights and identify patterns that may not be readily apparent to human analysts. Through its sophisticated algorithms, the LLM can provide businesses with predictive analytics, personalized recommendations, risk assessments, and other valuable information to drive strategic decisions and enhance operational efficiency. As companies increasingly rely on data-driven approaches to gain a competitive edge in today's fast-paced business landscape, the use of LLMs has become essential for organizations looking to harness the power of big data for informed decision-making #machinelearning #llm #data #algorithms #pythonprogramming #datascience #statistics #data #ai #engineering #mathematics #ml #transformation #visualization #apachekafka #mapreduce #hbase #technology #datamanagement #projects #nosql

Our latest editorial in Nature Machine Intelligence: What is in your LLM-based framework? We ask authors for increased transparency in describing their LLM-based research methods (for instance when using GPT-4) and to watch out for pitfalls in reproducibility. https://lnkd.in/e3BvADrM

I've been diving deep into Retrieval-Augmented Generation (RAG) solutions using .NET and Microsoft's Semantic Kernel. It's fascinating to see how these technologies are reshaping the way we approach information retrieval and natural language processing. One particularly challenging aspect has been integrating knowledge graphs into our RAG pipeline. While knowledge graphs offer incredible potential for enhancing context and relationships between data points, aligning them with the vector-based approach of modern language models is no small feat. Some key areas we're focusing on: Optimizing graph traversal algorithms for real-time query processing Balancing the trade-offs between graph complexity and query response times Developing heuristics to effectively combine graph-based and vector-based relevance signals. I'm curious: Are any other financial consulting professionals out there working on similar RAG implementations? How are you tackling the unique challenges our industry presents when it comes to integrating AI, especially knowledge graphs, into advisory processes? #ArtificialIntelligence #RAG #DotNet #SemanticKernel #KnowledgeGraphs #MachineLearning

💥💥💥 LLaVA-o1: The first visual language model capable of spontaneous, systematic reasoning, similar to GPT-o1! This 11B model outperforms Gemini-1.5-pro, GPT-4o-mini, and Llama-3.2-90B-Vision-Instruct! The key is training on structured data and a novel inference time scaling method—stage-level beam search. 👉 LLaVA-o1: Let Vision Language Models Reason Step-by-Step Guowei Xu, Peng Jin, Li Hao, Yibing Song, Lichao Sun, Li Yuan Abstract Large language models have demonstrated substantial advancements in reasoning capabilities, particularly through inference-time scaling, as illustrated by models such as OpenAI's o1. However, current Vision-Language Models (VLMs) often struggle to perform systematic and structured reasoning, especially when handling complex visual question-answering tasks. In this work, we introduce LLaVA-o1, a novel VLM designed to conduct autonomous multistage reasoning. Unlike chain-of-thought prompting, LLaVA-o1 independently engages in sequential stages of summarization, visual interpretation, logical reasoning, and conclusion generation. This structured approach enables LLaVA-o1 to achieve marked improvements in precision on reasoning-intensive tasks. To accomplish this, we compile the LLaVA-o1-100k dataset, integrating samples from various visual question answering sources and providing structured reasoning annotations. Besides, we propose an inference-time stage-level beam search method, which enables effective inference-time scaling. Remarkably, with only 100k training samples and a simple yet effective inference time scaling method, LLaVA-o1 not only outperforms its base model by 8.9% on a wide range of multimodal reasoning benchmarks, but also surpasses the performance of larger and even closed-source models, such as Gemini-1.5-pro, GPT-4o-mini, and Llama-3.2-90B-Vision-Instruct Paper 👉 arxiv.org/abs/2411.10440/ Code 👉 https://lnkd.in/dW5KgGfr #machinelearning

🚀 Unlocking the Power of Retrieval-Augmented Generation (RAG) in AI 🚀 Excited to share a comprehensive presentation on Retrieval-Augmented Generation (RAG) – a game-changer for integrating private data and optimizing contextual responses in large language models (LLMs)! This presentation dives deep into: 📊 Motivation for RAG: How RAG bridges the gap between public and private data, enhancing context-specific responses with expanded context windows. 🔍 Core Components: Indexing: Preparing external data for efficient retrieval Retrieval: Finding relevant documents using vector-based similarity search Generation: Producing coherent, data-backed responses 🧠 Advanced Techniques: Query Translation: Multi-query generation, decomposition, stepback prompting, and more Routing and Query Construction: Ensuring each query reaches the right data source Advanced Indexing: Multi-representation, hierarchical indexing, and token-level similarity with ColBERT 📈 Building a RAG Pipeline: A step-by-step guide for creating an end-to-end system for domain-specific knowledge retrieval, including data preparation, retrieval, generation, and dynamic validation. 💡 By implementing RAG, organizations can leverage their private, proprietary data securely and dynamically. This presentation provides both the theoretical foundations and practical steps to bring RAG to life! #AI #MachineLearning #LLMs #InformationRetrieval #DataScience #NLP #RAG #KnowledgeManagement #Innovation