Nyun AI’s Post

🚀 𝐀𝐈 𝐂𝐨𝐮𝐫𝐬𝐞 𝐛𝐲 Xeven Solutions: 𝐀 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐯𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞🚀 I’m thrilled to share my experience with Xeven Solutions' AI course, which has been immensely rewarding. I recently completed 𝑨𝒔𝒔𝒊𝒈𝒏𝒎𝒆𝒏𝒕 6, where I delved into: 🔍 𝑬𝒙𝒑𝒍𝒐𝒓𝒂𝒕𝒐𝒓𝒚 𝑫𝒂𝒕𝒂 𝑨𝒏𝒂𝒍𝒚𝒔𝒊𝒔 (𝑬𝑫𝑨): Loaded the Titanic dataset and explored its structure. Analyzed data types, unique categorical values, and missing values. 🛠️ 𝑯𝒂𝒏𝒅𝒍𝒊𝒏𝒈 𝑴𝒊𝒔𝒔𝒊𝒏𝒈 𝑽𝒂𝒍𝒖𝒆𝒔: Addressed missing values and removed duplicates. Ensured data integrity for accurate analysis. 📊 𝑫𝒆𝒔𝒄𝒓𝒊𝒑𝒕𝒊𝒗𝒆 𝑺𝒕𝒂𝒕𝒊𝒔𝒕𝒊𝒄𝒔: Calculated summary statistics and visualized correlations. 📁 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐌𝐲 𝐆𝐢𝐭𝐇𝐮𝐛: Check out all the assignment solutions from this course on my 🔗 𝐆𝐢𝐭𝐇𝐮𝐛 𝐏𝐫𝐨𝐟𝐢𝐥𝐞 [https://lnkd.in/dZCPc5u5] Follow for updates as I continue to advance in AI! Special thanks to Sir Muhammad Irfan , Sir Muhammad Haris Tariq , Dr. Sheraz Naseer - (PhD Artificial Intelligence, Data Science) , and the entire 𝑿𝒆𝒗𝒆𝒏 𝑻𝒆𝒂𝒎 for their invaluable insights and exemplary teaching. Their dedication has made complex concepts accessible and engaging.

🌟 Exciting Announcement! 🌟 I am thrilled to share the completion of my latest project on Dry Bean Classification using the powerful K Nearest Neighbor (KNN) Algorithm, as part of my Machine Learning course at Innomatics Research Labs! 🌱 Project Overview: In this project, I delved into the fascinating world of machine learning to develop a robust classification model for identifying different types of dry beans. Leveraging the KNN Algorithm, I explored the intricate patterns within the dataset to accurately classify dry beans based on their unique characteristics. 💡 Key Highlights: Thorough exploration of the KNN Algorithm, its principles, and its application in classification tasks. Extensive preprocessing and feature engineering to ensure data quality and model performance. Strategic partitioning of the dataset into training and testing subsets for robust evaluation. Hyperparameter tuning to optimize the KNN model for maximum accuracy and efficiency. Rigorous validation and performance assessment using industry-standard metrics, including accuracy and log loss. 🚀 What I Learned: This project has been an enriching journey, allowing me to hone my skills in data preprocessing, model selection, and performance evaluation. I've gained valuable insights into the nuances of machine learning algorithms and their practical applications in real-world scenarios. https://lnkd.in/gvWKS_-V Raghu Ram Aduri #ml #ai

🚀 Mastering Hyperparameter Optimization: Unleash the Full Potential of Your Models! 🚀 In the realm of machine learning, the power of a model isn't just in its architecture or the data it's trained on—it's also in the fine-tuning of its hyperparameters. 🧠✨ 🔍 What are Hyperparameters? Hyperparameters are the settings that you configure before the learning process begins. They control the training process and the structure of the model, influencing how the model learns and performs. Examples include learning rate, number of layers, and batch size. 🔧 Why Optimize? Optimizing hyperparameters can significantly enhance model performance, turning a good model into a great one. It's akin to tuning a car engine for maximum efficiency and speed. 🛠️ Popular Techniques for Hyperparameter Optimization: Grid Search: An exhaustive search technique that tests all possible combinations of hyperparameters. Simple but computationally expensive. Random Search: Instead of trying all combinations, it samples a fixed number of hyperparameter settings from a specified distribution. Often finds good solutions faster than grid search. Bayesian Optimization: Builds a probabilistic model of the function mapping hyperparameters to model performance and uses this to select the most promising hyperparameters to evaluate next. Efficient and powerful, especially for expensive-to-evaluate functions. Hyperband: An adaptive resource allocation and early-stopping strategy to quickly converge to a good set of hyperparameters. Reduces computational cost significantly. Genetic Algorithms: Inspired by the process of natural selection, this technique uses operations such as mutation, crossover, and selection to evolve the hyperparameters over successive generations. Effective for large and complex search spaces. 📈 Choosing the Right Technique: The choice of technique depends on the problem, computational resources, and time constraints. For small datasets or simpler models, grid or random search might suffice. For more complex models, Bayesian optimization or genetic algorithms can provide superior results. 🔥 Stay Ahead in the Game: In today's competitive landscape, mastering hyperparameter optimization can set you apart. It’s not just about building models—it's about building the best models. 🌟 #MachineLearning #DataScience #AI #HyperparameterOptimization #GridSearch #RandomSearch #BayesianOptimization #GeneticAlgorithms #Hyperband #ModelTuning #TechInnovation #DataDriven #csvtu #csvtu_utd #programming

Optimizers #5 Adagrad (Adaptive Gradient Algorithm) An optimization method that adjusts the learning rate for each parameter based on the historical gradients. It is useful for sparse data and helps in automatically adjusting the step size, leading to more efficient and stable convergence. In Simple Terms Imagine you are walking downhill, trying to reach the lowest point (valley). In standard gradient descent, you take steps of a fixed size. However, in Adagrad, the step sizes change based on how steep the path is. How It Works: Adagrad adapts the step size for each parameter based on how frequently it has been updated. Parameters that get updated frequently have their step sizes reduced, while parameters that are updated less frequently have larger step sizes. This helps in making sure you don't take too large steps in steep areas and take appropriately sized steps in flatter areas. Where it is most usefull- Adagrad is particularly useful for sparse data (ex- text data) where some features are very frequent and others are rare. It automatically adjusts the learning rate, so you don't have to manually tune it. NOTE- It works well in convex optimization problem like linear regression but when we are are working with non convex optimization or complex neural network, it not be able to converge to global minima. #MachineLearning #ArtificialIntelligence #DeepLearning #Optimizers #Statistics #AdaGrad

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

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

🔍 Unlock the power of Gradient Descent in #MachineLearning! 🚀 Our latest YouTube video delves into this fundamental optimization technique, essential for fine-tuning models and achieving better results. Ready to elevate your ML skills? Dive in now! #GradientDescent #AI #DataScience #Algorithm #YouTube #MLCommunity 🧠📊💡 Watch here: [https://lnkd.in/gnP2aezs]

Interesting blog post describing Met Office approach to ML-based weather forecasts. Combining ML models trained on ERA5 & on past UKV high res forecasts will allow local scale ML predictions, with ensembles. https://lnkd.in/ecH-apn2

In a world increasingly dominated by climate change impacts and ever more extreme weather events rapid advances in artifical intelligence and machine learning are coming at just the right time to give society the best chance of maintaining resilience.

🚀 Unlocking the Power of Variational Autoencoders in Time Series Analysis! 📊 I'm excited to share my latest Substack post, where I dive deep into the fascinating world of Variational Autoencoders (VAEs) and their application in generating synthetic time series data. 🔍 What You'll Discover: A clear explanation of how VAEs work, including the encoder and decoder architecture. Step-by-step code to implement a VAE for time series data generation, complete with visualizations of the results! This post is perfect for data scientists, machine learning enthusiasts, or anyone curious about generative models and their practical applications. Let’s unlock new insights in data generation together! 🌟 #MachineLearning #DataScience #VariationalAutoencoder #TimeSeries #AI #DeepLearning #DataGeneration