Neurons Lab’s Post

In today's fast-paced world, real-time access to relevant information is critical. But speed isn't everything; we need the right answer quickly. In my Retrieval-Augmented Generation (RAG) system for contextual information retrieval from PDFs, I've achieved faster retrieval with Hierarchical Navigable Small World (HNSW) which uses efficient nearest neighbor search. HNSW constructs a multi-layered graph structure where data points are connected based on similarity. This allows for efficient navigation through the data, enabling faster retrieval of relevant information compared to linear search methods like cosine similarity. In my testing, I've uploaded the QLoRA research paper (https://lnkd.in/gvjfEJRS) having around 1800 sentences. With usual cosine similarity the retrieval time was 0.5 seconds, while HNSW took just 0.0012 seconds. My project: https://lnkd.in/g-Ws9XRi Connect for more! I'm excited to explore the future of Generative AI and join with other passionate individuals and teams in the field! #RAG #GenerativeAI #MachineLearning #GeminiAPI #LLMs #InformationRetrieval #search #hnsw

The Rise of LLMs with Knowledge Graphs: Unlocking Real-Time Insights 👇 : - Enhanced Search and Recommendation Systems: LLMs with knowledge graphs can understand the context and relationships between entities, leading to more relevant and personalized search results and product recommendations. - Real-Time Fact-Checking and Q&A Systems: These models can access and verify information within the knowledge graph, enabling real-time fact-checking and providing accurate answers to user queries. - Dynamic Chatbots and Virtual Assistants: Knowledge graph-powered LLMs can hold more nuanced conversations, understanding the context of the dialogue and leveraging external knowledge for more helpful responses. However, it's important to acknowledge the limitations 👇 : - Data Bias: Knowledge graphs can reflect the biases present in their source data. Addressing this ensures fairer and more accurate results. - Knowledge Gap: The knowledge graph may not capture everything, leading to potential errors if the LLM encounters information outside its scope. - Explainability: Understanding how the LLM arrives at an answer based on the knowledge graph can be challenging, limiting interpretability. Overall, the potential benefits of LLM knowledge graph combinations are undeniable. By acknowledging and addressing the limitations, we can unlock a future of powerful, real-time data-driven insights. Let's continue this conversation! What are your thoughts on this exciting technology? #LLM #KnowledgeGraph #AI #RealTime #DataScience #GenAI #FutureofTech 

🌟 Explore the Power of Stochastic Gradient Descent in ML/AI! 🚀 Imagine navigating a dense forest, searching for the optimal path amidst uncertainty. Stochastic Gradient Descent (SGD) serves as your agile guide, dynamically adjusting your route to converge towards the best solution. In Machine Learning and Artificial Intelligence, SGD plays a crucial role in fine-tuning models, allowing them to learn efficiently from vast datasets. 🔗 Discover more: https://lnkd.in/g5-ZaRCH #AI #MachineLearning #StochasticGradientDescent #Calculus #Optimization #Tech #DeepLearning #Science #DataScience #PrayingMantis #DeBug 🧮🚀

🔍 Handling Imbalanced Datasets in Machine Learning 🔍 In the real world, datasets are often imbalanced, meaning some classes are significantly underrepresented. This is common in scenarios like distinguishing between genuine and fraudulent e-commerce transactions, where genuine transactions far outnumber fraudulent ones. When using algorithms like Support Vector Machines (SVM) with a soft margin, we can define different costs for misclassified examples to better handle this imbalance. 💡Key Insight: - Adjusting Misclassification Costs: By setting higher costs for misclassifying minority class examples, we force the model to pay more attention to these cases. This approach helps in achieving a more balanced decision boundary, as illustrated in the figure below:   - Figure 1a: Equal weight for both classes leads to misclassification of minority class examples.   - Figure 1b: Higher weight for the minority class reduces misclassification at the cost of the majority class. Some SVM implementations allow assigning weights to each class, which the learning algorithm uses to find the optimal hyperplane, improving the model's performance on imbalanced datasets. Understanding and addressing imbalanced datasets is crucial for building robust and fair machine learning models. Have you encountered this challenge in your projects? Share your experiences and solutions in the comments! #MachineLearning #DataScience #ImbalancedData #SVM #AI #TechInsights #LearningJourney #DataAnalysis

🟢 𝗕𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝘃𝗲𝗿𝘆 𝗼𝘄𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗳𝗿𝗼𝗺 𝘀𝗰𝗿𝗮𝘁𝗰𝗵! Building your own AI Agents with wrappers and libraries doens't have to be hard. Here's a quick crash course hands-on guide on how to build your own AI Agents completely from Scratch without any libraries used for agents. AI Agents are basically a combination of LLMs, tools and prompts. You make the LLM reason to our prompts, understand which tools to call, execute the tools, get the result and make the LLM reason again to see if it has enough information to answer our question. _________________________ To download this PDF, hover over the image, click the full-screen icon, and then click the download icon. Source: DJ Sarkar via Dipanjan S. #GenAI #GenerativeAI #artificialintelligence #machinelearnning #datascience _________________________ 🟡 Browse my feed 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗳𝗲𝗲𝗱: https://lnkd.in/fFpym45 for other great resources 🔴 Visit my blog "𝗘𝘅𝘁𝗿𝗲𝗺𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴" https://lnkd.in/g5d965kh. Topics include: * 𝗧𝗵𝗲 𝘂𝗻𝗿𝗲𝗮𝘀𝗼𝗻𝗮𝗯𝗹𝗲 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗻𝗲𝘀𝘀 𝗼𝗳 𝗾𝘂𝗮𝘀𝗶𝗿𝗮𝗻𝗱𝗼𝗺 𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀 https://lnkd.in/gfiG_Edx * 𝗔 𝗻𝗲𝘄 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 𝗳𝗼𝗿 𝗺𝘂𝗹𝘁𝗶-𝗮𝗿𝗺𝗲𝗱 𝗯𝗮𝗻𝗱𝗶𝘁𝘀 https://lnkd.in/gCTnWFvb * 𝗛𝗼𝘄 𝘁𝗼 𝗲𝘃𝗲𝗻𝗹𝘆 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲 𝗽𝗼𝗶𝗻𝘁𝘀 𝗼𝗻 𝗮 𝘀𝗽𝗵𝗲𝗿𝗲 https://lnkd.in/gEu6HjxZ * 𝗛𝗼𝘄 𝘁𝗼 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝘂𝗻𝗶𝗳𝗼𝗿𝗺𝗹𝘆 𝗿𝗮𝗻𝗱𝗼𝗺 𝗽𝗼𝗶𝗻𝘁𝘀 𝗼𝗻 𝗵𝘆𝗽𝗲𝗿𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹 𝘀𝗽𝗵𝗲𝗿𝗲 𝗮𝗻𝗱 𝗯𝗮𝗹𝗹𝘀 https://lnkd.in/gPmE5xMQ * and many more! ;) Enjoy! Martin

📢 Don't miss out ‼ 📢 Retrieval augmented generation (RAG) and text-to-SQL are among the hottest topics in generative AI apps today. Join our webinar this Wednesday to learn how to use these techniques to ground LLMs with enterprise data.

🐄 Bridging the Gap in LLM Production: RAG vs. Fine-Tuning 🐄 If you’ve ever felt like an AI engineer stuck in a strange gap between RAG (Retrieval-Augmented Generation) and Fine-Tuning, this one’s for you! Here’s the reality: 🔧 RAG brings fresh data into the game, perfect for dynamic information needs. ⚙️ Fine-Tuning helps your model get smarter by adapting it to your specific use case over time. But… to get that Production-Ready LLM App out, you’ll often need both techniques working hand in hand! And yes, it might feel like navigating a gap sometimes—but mastering the balance is key to deploying powerful, efficient AI systems. Ready to cross that bridge? Join me here : Course Curriculum - https://lnkd.in/gEQGfkbW Enrollment Link - https://lnkd.in/g3Tg42Zi Coupon Code - EARLY50 (50% Flat OFF) Course Start Date - 28th September 2024(Weekends only) Class Timing - 3-5 PM IST #AI #MachineLearning #RAG #FineTuning #AIEngineering #LLM #AIProduction #GenerativeAI #GrowDataSkills

💻 Leveraging ROC and AUC with SVM, GaussianNB, DecisionTreeClassifier, and XGBoost in Machine Learning 💻 👨💻 In the world of machine learning, selecting the right model is key, but evaluating it effectively is where the magic happens. Two metrics that are crucial for evaluating classifiers are ROC (Receiver Operating Characteristic) and AUC (Area Under the Curve). Let’s dive into how these metrics play out with some popular algorithms: SVM, GaussianNB, DecisionTreeClassifier, and XGBoost. And all the model is did with 85% recall. With all this model what I found is XGBoost is most effect model in machine Learning. 🔍 Understanding ROC and AUC The ROC curve is a graphical representation of a model's performance across different threshold settings, plotting the True Positive Rate against the False Positive Rate. The AUC provides a single value summary of the ROC curve, with values closer to 1 indicating superior model performance. ⚙️ Applying ROC and AUC to Different Models: Support Vector Machine (SVM): SVMs are powerful for classification tasks, especially with high-dimensional data. By tuning the kernel and regularization parameters, you can achieve a high AUC, making SVM a robust choice when working with complex datasets. Gaussian Naive Bayes (GaussianNB): GaussianNB is known for its simplicity and efficiency, especially in high-dimensional spaces. Although it assumes feature independence, it can still perform well with an optimized threshold, reflected by a solid AUC. DecisionTreeClassifier: Decision Trees are intuitive and easy to interpret. However, they might overfit, leading to a lower AUC. Pruning and adjusting the max depth are crucial steps in fine-tuning to improve performance. XGBoost: XGBoost is a go-to for many Kaggle champions, known for its scalability and performance. Fine-tuning parameters like learning rate, max depth, and number of estimators can push the AUC higher, making it an excellent choice for competitive modeling. #MachineLearning #DataScience #ModelEvaluation #SVM #GaussianNB #DecisionTreeClassifier #XGBoost #ROC #AUC #FineTuning #AI #DataAnalysis

👋 Hi everyone, For Data Jam episode 13, our topic will be "HCI meets AI/ML paper study: Generative Agents Simulacra". This paper presents generative agents, which are software entities designed to mimic realistic human actions. The framework enhances a sophisticated language model to encode and reproduce agents’ memories, experiences, and intentions using natural language. References: - https://lnkd.in/gWdwsWG7 - https://lnkd.in/dkJsjzMx What is the event format❓️ Basically, we're going to be exploring and studying a specific topic and hopefully it will create a discussion around that topic. See you on our discord server. If you haven't join the server, check the invitation link on below. Discord link: https://lnkd.in/gca6s8ug For Data Jam event reminder, you can register through this link: https://lnkd.in/gKbbGnfx And we will invite you to our google calendar so you won't miss it! #datascience #machinelearning #artificialintelligence

How will time-series forecasting evolve (and improve) amid the rise of LLMs and generative-AI tools? Marco Cerliani wonders if simple methods might out-perform complex approaches, and aims to understand how to carry out zero-shot time-series forecasting with standard machine learning models.