Shifa Zahra’s Post

🚀 Project Update: XGBoost Classification and Multi-threading 🚀 This month, our team at Yvolution has been focused on advancing our project with two powerful technologies: XGBoost classification and multi-threading. Despite facing some complex challenges, we've made significant progress that we’re excited to share! 🔍 XGBoost Classification: XGBoost is a highly efficient and flexible machine learning algorithm that excels in classification tasks. Here's what we've been up to: Improved Accuracy: By fine-tuning hyperparameters and leveraging the power of gradient boosting, we've enhanced the accuracy of our classification models. Handling Large Datasets: XGBoost’s ability to handle large datasets has been crucial in processing and analyzing the vast amounts of data we work with. Feature Engineering: We've focused on creating new features and selecting the most impactful ones, which has significantly improved model performance. 🧵 Multi-threading: Implementing multi-threading has been a game-changer for our computational tasks: Speed and Efficiency: Multi-threading has drastically reduced the time required for training our models, allowing us to iterate faster. Complex Workflows: By parallelizing our processes, we've managed to handle more complex workflows efficiently, making the most out of our computational resources. Scalability: This approach has improved the scalability of our solutions, ensuring that we can handle increasing amounts of data without compromising performance. ⚠️ Challenges and Solutions: While working with XGBoost and multi-threading, we've encountered several challenges: Memory Management: High memory usage was a hurdle, but optimizing data structures and using efficient memory management techniques have helped us overcome it. Synchronization Issues: Ensuring proper synchronization in multi-threaded environments was tricky, but using advanced synchronization mechanisms and careful thread management has resolved most of these issues, however we are still improving this area. 🌟 Looking Ahead: Our progress this month has laid a strong foundation for future developments. We’re excited to continue exploring these technologies and pushing the boundaries of what's possible. How have you leveraged XGBoost or multi-threading in your projects? Share your experiences and insights below! #Yvolution #XGBoost #MachineLearning #MultiThreading #TechInnovation #DataScience #AI #ProjectUpdate

Week -5 of RAG Ever wondered how does indexing works in semantic search while using pg vector database? Unlocking the potential of HNSW indexing can dramatically speed up nearest neighbor searches in high-dimensional spaces. By structuring embeddings into a layered, graph-based format, HNSW connects nodes based on proximity, using small-world properties for quick navigation. This setup cuts down on the number of comparisons, making retrieval much faster than traditional methods. Plus, HNSW allows for easy updates, so you can add or remove embeddings without overhauling the entire index. It's a game-changer for managing large datasets with complex vector embeddings. It looks like at the end everything revolves around Data structures and algorithm whether is AI or something else.😅 #OPENAI #RAG #PGVECTOR #EMBEDDINGS

Kolmogorav-Arnold Networks: Over the past week you may have heard about Kolmogorav-Arnold Networks (KANs) as a promising new machine learning architecture that is going to replace the Multi-Layer Perceptron (MLP) and revolutionize the way Machine learning is done forever. But let me tell you for now, the MLP is not going anywhere.  KANs appear to promise the following things: 👉 KANs can achieve superior accuracy with fewer parameters compared to MLPs. This indicates that they can provide better results while being more computationally efficient. 👉 KANs have learnable activation functions on edges (weights), while MLPs have fixed activation functions on nodes (neurons). 👉 KANs offer enhanced model interpretability. 👉 KANS avoid catastrophic forgetting.   The part of the KAN paper that I haven't seen people talk very much about 👉 As of right now, the method doesn't scale. When the KAN has the same number of parameters will be much slower (10x slower) because the model has to model splines where are more computationally expensive than linear combinations. 👉 Learnable activation functions already exist. Take a look at the Gated Linear Unit (GLU), or GeGLU, or, my personal favorite, SwiGLU. The application is slightly different but it exists. 👉 Interpretability? Okay, not gonna lie, this is pretty cool even if it doesn't scale well. To be able to pull symbolic formulas would be amazing for anyone working on Physically constrained problems 👉 Catastrophic forgetting: Remember that this model is using splines. Splines have all sorts of edge effects and issues with derivatives and continuity. Especially in higher dimensions, it's unclear to me how this would change or be a non-issue.     Now, do I hate this work? Absolutely not. It's a cool idea, the authors really challenge the foundational approaches to machine learning. However, I don't think, as it is, that KANs are going to be the new thing that revolutionizes AI as we know it. And we should stop sharing it as that. Now that you've seen the good and bad on KANs, What are your thoughts on KANs?   #LLM #LLMs #MachineLearning #DeepLearning #DSwithSaul

🚀 𝐔𝐧𝐯𝐞𝐢𝐥𝐢𝐧𝐠 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐕𝐞𝐜𝐭𝐨𝐫 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 (𝐒𝐕𝐌) 𝐢𝐧 𝐒𝐢𝐦𝐩𝐥𝐞 𝐓𝐞𝐫𝐦𝐬! 🧠 Ever heard of Support Vector Machine (SVM)? It might sound complex, but let's break it down! 🔍 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐒𝐕𝐌? SVM is a powerful machine learning algorithm used for classification and regression tasks. It works by finding the optimal hyperplane that best separates different classes in a dataset. 💡 𝐑𝐞𝐚𝐥-𝐋𝐢𝐟𝐞 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Imagine you're a farmer trying to classify fruits into apples and oranges based on their size and color. SVM would help you draw a boundary (hyperplane) between the two types of fruits in such a way that the gap between the closest points from each class (support vectors) is maximized. This boundary ensures accurate classification of new fruits. 🔑 𝐊𝐞𝐲 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 𝐨𝐟 𝐒𝐕𝐌: 1️⃣ Effective in high-dimensional spaces 2️⃣ Versatile - works well with both linear and non-linear data 3️⃣ Robust against overfitting when properly tuned 🛠️ 𝐇𝐨𝐰 𝐢𝐭 𝐖𝐨𝐫𝐤𝐬: 1️⃣ Collect and preprocess your data. 2️⃣ Choose the appropriate kernel function (linear, polynomial, or radial basis function) to transform your data into higher dimensions if needed. 3️⃣ Train the SVM model to find the optimal hyperplane. 4️⃣ Test your model on new data to evaluate its performance. 🌟 𝐑𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: SVM finds applications in various fields such as: - Image classification - Text categorization - Bioinformatics - Finance 💬 𝐋𝐞𝐭'𝐬 𝐂𝐨𝐧𝐧𝐞𝐜𝐭! Excited to delve deeper into the world of SVM? Let's discuss its applications and explore how it can revolutionize your business! Feel free to reach out and start the conversation. #machinelearning #datascience #ai #svm #supportvectormachine #technology #classification #regression #algorithms #irfanmalik #xevensolutions #learningandgrowing

𝐃𝐚𝐲 6 𝐨𝐟 #60𝐃𝐚𝐲𝐬𝐀𝐈 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐰𝐢𝐭𝐡 Srajal Tiwari 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 Today, we're exploring three machine learning powerhouses: Decision Trees, Random Forests, Support Vector Machines (SVM). Each has unique strengths and can drive exceptional results depending on your problem. 1. 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐓𝐫𝐞𝐞𝐬 🌳 What They Are: Decision Trees are flowchart-like structures where each internal node represents a feature (or attribute), each branch represents a decision rule, and each leaf node represents an outcome. It’s a popular algorithm due to its simplicity and interpretability. 2. 𝐑𝐚𝐧𝐝𝐨𝐦 𝐅𝐨𝐫𝐞𝐬𝐭𝐬 🌲 What They Are: A Random Forest is an ensemble of Decision Trees. It leverages the power of multiple trees to make a more robust and accurate prediction by averaging the results (for regression) or taking a majority vote (for classification). 3. 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐕𝐞𝐜𝐭𝐨𝐫 𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐬 (𝐒𝐕𝐌) 📏 What They Are: SVM is a supervised learning algorithm mainly used for classification. It works by finding the hyperplane that best separates different classes in the feature space. In cases where the data isn’t linearly separable, SVM can use kernel functions to project data into a higher dimension. Choosing the Right Algorithm: -Decision Trees and Random Forests are great starting points, especially if you need interpretability or are working with a mixed dataset. - SVM is ideal for high-dimensional data or cases with clear margins between classes. Mastering these algorithms will boost your AI project performance! Share your favorite algorithm and its applications! #60DaysAI #MachineLearning #DecisionTrees #RandomForests #SVM #GradientBoosting #AIAgorithms #ArtificialIntelligence

Exploring LightRAG: An Efficient and Cost-Effective Solution for Retrieval Augmented Generation (RAG) I recently explored LightRAG, an innovative approach demonstrating improved efficiency and cost-effectiveness compared to existing methods like GraphRAG in the RAG domain. Existing RAG systems often fall short in maintaining contextual coherence and capturing complex relationships due to their reliance on flat data representations. LightRAG introduces an innovative approach by integrating graph structures with text indexing and retrieval, revolutionizing how information is processed and delivered. Key Features of LightRAG: 🔹Graph-Based Knowledge Integration: Graph structures allow LightRAG to capture intricate interdependencies among entities, enhancing contextual awareness and providing more coherent and comprehensive responses. 🔹Dual-Level Retrieval System: Low-Level Retrieval focuses on specific entities and relationships for precise information. High-Level Retrieval addresses broader topics and themes, synthesizing information into contextually rich insights. 🔹Optimized Key-Value Structures: Allows rapid and precise retrieval. 🔹Incremental Update Algorithm: Ensures real-time data adaptation, keeping responses accurate in rapidly changing environments. Impactful Outcomes: Significant improvements in retrieval accuracy and efficiency. Fast response times while maintaining comprehensive results. LightRAG’s architecture combines the best of graph and vector-based knowledge, offering unparalleled efficiency and adaptability. Github: https://lnkd.in/dYACjR-t Paper: https://lnkd.in/dCuSCC2Z #AI #MachineLearning #LightRAG #RAG #GraphTechnology #DataScience #Innovation #LLM #ArtificialIntelligence #TechInnovation

I've had some great discussions with Daniel Svonava over the past several months around recommendation engines, semantic search, and vector databases. Tune in to learn more about all the advancements made last year around GenAI and how the space will continue to evolve in 2024 ☕

🌟 "Are you interested in GenAI, RAG, or Vector DBs? 🤔 Specifically, in how they have evolved interconnectedly over the last year and what this means for 2024? 🚀 Join Daniel Svonava, Laurie Voss, and Ankit Khare as they delve into the 'why' and 'what' 🧐 of the technological advancements in #RAG, #VectorSearch, and #VectorDBs. ✅ Ankit will explore last year’s GenAI timeline, focusing on vector databases, and delving into real-world AI use cases that blend structured and unstructured data capabilities. ✅ Daniel will discuss the data engineering and compute considerations ✅ Laurie will unpack how LlamaIndex is revolutionizing LLM applications through data integration and addressing the intricacies within RAG. ✅ Engage in a Q&A: Bring your questions and get them answered. 🔗Reserve your spot: https://lnkd.in/gYRKV3a2 #GenAI #RAG #VectorDB #AI #DataScience #MachineLearning #TechWebinar

Our paper "FedRepOpt: Gradient Re-parametrized Optimizers in Federated Learning" has been accepted at the #ACCV2024. We introduce a FedRepOpt optimizer for Federated Learning that boosts model performance on low-end devices by modifying optimizer gradients based on model-specific hyperparameters, achieving faster convergence times and robustness across different data distributions. Highlights: -The first systematic study on training RepOpt-based models in FL settings sheds light on their importance and sets a baseline for FL implementation. -We propose FedRepOpt and validate its effectiveness under different FL configurations. Our experiments demonstrate that models using FedRepOpt significantly boost performance by 16.7% and 11.4% over RepGhost-style and RepVGG-style networks, with accelerated convergence times of 11.7% and 57.4%. -We show that FedReOpt excels in cross-silo scenarios and cross-device setups, even in non-iid cases. 📄 Preprint: https://lnkd.in/gjkvZY9i Our implementation is based on the 🌼 Flower framework (Flower Labs). Feel free to check out our code using the following link. 🖥️ Code: https://lnkd.in/ghi2wQp4 Work done with Yasar Abbas Ur Rehman, Pedro Porto Buarque de Gusmão, Lai-Man Po, Ruby Ma, Yuyang Xie #TCLAILAB #AI #FederatedLearning #FL #ACCV2024 #ComputerVision #Reparameterization #CNN #FLOWER

🚀 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐒𝐭𝐚𝐭𝐞-𝐨𝐟-𝐭𝐡𝐞-𝐀𝐫𝐭 (𝐒𝐎𝐓𝐀) 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬: 𝐀 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 𝐨𝐟 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 🚀 The journey to designing SOTA architectures is both exhilarating and challenging, blending cutting-edge technology with real-world applications. Recently, I had the opportunity to work on projects involving YOLOv8, OpenAI CLIP, and advanced visualizations using t-SNE and UMAP, where precision and adaptability were essential. The key to building SOTA systems lies in: 🔹 𝐃𝐞𝐞𝐩 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐨𝐟 𝐭𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 – Identifying core objectives and challenges early on allows you to shape the architecture around specific needs. 🔹 𝐋𝐚𝐲𝐞𝐫𝐞𝐝 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 – Using custom models, such as YOLO for targeted detections, ensures each element of the system performs optimally. 🔹 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐟𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲 – SOTA architectures must be designed to evolve, adapting as new data and technologies emerge. In today's fast-paced tech landscape, creating SOTA architectures is essential to staying competitive. By combining cutting-edge research with real-world applications, we can bring unprecedented efficiency and performance across domains. Building these architectures often involves:    🔹 𝐈𝐧-𝐝𝐞𝐩𝐭𝐡 𝐌𝐨𝐝𝐞𝐥 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧: Choosing the right base models like YOLOv8, CLIP, or custom neural networks. 🔹 𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬: Streamlining data with libraries and frameworks to ensure quality and relevance. 🔹 𝐌𝐨𝐝𝐞𝐥 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Applying techniques like quantization, distillation, and hyperparameter tuning for maximum efficiency. 🔹 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐚𝐧𝐝 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧: Leveraging metrics, feedback loops, and real-world validation to fine-tune and improve. It’s challenging but rewarding work that allows us to harness the power of AI and machine learning for real-world impact. 💪 These projects have underscored the importance of harnessing advanced tools and frameworks to achieve robust, real-world solutions. #MachineLearning #DeepLearning #AI #Innovation #SOTA #YOLO #CLIP #UMAP #DataScience #Innovation #TechDevelopment #NeuralNetworks #ComputerVision