Yi Wang’s Post

Mastering Hyperparameter Tuning for Optimized Machine Learning Models Hyperparameter tuning is the secret sauce that transforms a good machine learning model into a great one. By fine-tuning parameters like learning rate, tree depth, or number of layers, you can maximize performance and accuracy. Key Highlights from the Article: 1. What is Hyperparameter Tuning? A method to optimize non-learnable parameters in a machine learning model. Impacts training speed, convergence, and overall accuracy. 2. Techniques for Tuning: Grid Search: Systematic exploration of parameter combinations. Random Search: Random sampling of hyperparameters for efficiency. Bayesian Optimization: Intelligent exploration for fewer iterations. 3. Practical Steps with Code: Learn how to implement tuning using libraries like Scikit-learn, TensorFlow, or PyTorch. Understand real-world examples of hyperparameter tuning in action. 4. Challenges: Time-consuming process for large datasets. Risk of overfitting when tuning excessively. https://lnkd.in/gkBMQ4vc Additional Resources: Tools for Automation: Optuna, Ray Tune https://lnkd.in/gExMuTnF Code Examples: Explore hyperparameter optimization on GitHub. https://lnkd.in/g3n_SQbp #MachineLearning #HyperparameterTuning #AI #DataScience #MLModels #OptimizationTips

“Sharing is Caring” Hi #AI Community!!! #sharingiscaring “Send me an Invite and get your skills endorsed straightaway as pleasantries !” “#Knowledge Booster” 📚 Expand your knowledge base with this helpful article..Keep #Learning !! 👍 NOTE : This is NOT a paid Advertisement #Robotics #artificialintelligence #dataanalytics #datascience #machinelearning #deeplearning #linkedinfamily #knowledgesharing #machinelearningalgorithms #neuralnetworks #deeplearning #deeplearningai #computervision Join me on my way into an exciting world of Data/Analytics/AI Send me an Invite and get your skills endorsed straightaway as pleasantries 😊 🍁Let’s Connect Now

Unveiling the Power of Baseline Models in Machine Learning While complex architectures and cutting-edge techniques rightfully captivate the imagination in machine learning, the foundation of any successful model lies in the humble baseline. In the vast landscape of machine learning (ML), where complex algorithms and sophisticated architectures often steal the spotlight, it’s easy to overlook the humble yet crucial baseline models. These unassuming models serve as the foundation upon which more advanced solutions are built. In this article, we’ll delve into the world of baseline models, demystify their purpose, and explore why they are essential in ML development pipelines. Read more: https://lnkd.in/dEsmakVr

How does MLX on Metal perform in handling machine learning tasks? Yi Wang and I conducted a set of benchmarks using M1 Max, M2 Ultra with MLX, A100, and H100 with PyTorch to compare the performance of two fundamental operations, SDPA and Linear Projection. A surprising revelation is the close performance between the M2 Ultra and A100, underscoring the impressive potential of on-device machine learning. The benchmark also reveals distinct performance trends. Linear Projection shows a linear increase in latency with larger input sizes, while SDPA exhibits exponential latency growth due to its higher complexity. Interestingly, the performance disparity in SDPA is much less pronounced than in Linear Projection. For instance, Linear Projection demonstrates a nearly 100x performance difference between the M1 Max and H100, whereas SDPA shows only 25x difference on the same set of hardwares. These findings highlight the significant potential of on-device machine learning, and we look forward to further enhancements in performance, particularly with advancements in Metal.

Considering the price ratio (1 : 14.3) and the TDP ratio (estimated in 1 : 20) for M1 max vs workstations with H100, these results seem promising and make me believe I will see ubiquitous robotics in my life span.

Machine learning can potentially automate tedious tasks and increase productivity by 50% or more in some cases. Yet, there are challenges that arise and shut down ML pilots before takeoff. In the following article, CTO Lior Gavish examines the four most common reasons Machine Learning pilots fail to take off and how to avoid these pitfalls. Read more: https://lnkd.in/g5EcnQhT Source: Techopedia #machinelearning #optimization

How Bayes’ Theorem is Applied in Machine Learning Bayes’ theorem tells use how to gradually update our knowledge on something as we get more evidence or that about that something.

Machine learning model optimization is a dynamic and critical aspect […]

L2 Regularization: Beyond Overfitting in Machine Learning 💥💥 GET FULL SOURCE CODE AT THIS LINK 👇👇 👉 https://lnkd.in/d4BDUrAi L2 regularization, also known as Ridge Regression, is a popular approach in machine learning for preventing overfitting and improving model generalization. But its applications go beyond this: it also helps in feature selection, collinearity reduction, and stabilizing the coefficients, enhancing model performance. In this post, we dive deeper into the world of L2 regularization. We discuss its mathematical foundation, the underlying motivation of adding the L2 regularization term to the cost function, and the impact of various regularization parameters. Additionally, we compare L2 to L1 regularization and discuss their differences in terms of feature selection and model performance. To explore L2 regularization further, study the following resources: 1. Andrew Ng's Machine Learning course: https://lnkd.in/duN88raz 2. "The Elements of Statistical Learning: Data Mining, Inference, and Learning Algorithms" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Additional Resources: - "An Introduction to Statistical Learning" available at: https://lnkd.in/dxi9TXn8 - The scikit-learn documentation on Ridge Classifier and Ridge Regression can be found at: https://lnkd.in/dHMvMa6D #STEM #MachineLearning #L2Regularization #RidgeRegression #Overfitting #FeatureSelection #ModelPerformance #DataMinig #MachineLearningAlgorithms #Technology #AI #AIAlgorithms Find this and all other slideshows for free on our website: https://lnkd.in/d4BDUrAi #STEM #MachineLearning #L2Regularization #RidgeRegression #Overfitting #FeatureSelection #ModelPerformance #DataMinig #MachineLearningAlgorithms #Technology #AI #AIAlgorithms https://lnkd.in/d8RgunGf

Machine Learning: Understanding Hinge Loss and Its Role in SVM Optimization 💥💥 GET FULL SOURCE CODE AT THIS LINK 👇👇 👉 https://lnkd.in/dbiSDKN9 Machine Learning has become a cornerstone of modern technology, allowing systems to learn from data and make predictions or decisions. In this explanation, we will dive into the concept of Hinge Loss and its significance in Support Vector Machines (SVM) optimization. Hinge Loss is a popular loss function in Machine Learning, particularly in the context of SVMs. Its main goal is to find the best-fit hyperplane that maximally separates data points from different classes. The function introduces a margin, quantifying the distance between the hyperplane and the closest data points from each class. When the margin is large, the model is considered to have a good generalization ability. Additionally, Hinge Loss employs soft-margin techniques, which allow some data points to lie on the wrong side of the hyperplane. These misclassifications, however, are penalized with a loss. By minimizing the Hinge Loss, we optimize the SVM model to find the hyperplane that separates the data points efficiently while maximizing the margin. It is essential to understand the underlying mathematics and meaning of Hinge Loss to take full advantage of the SVM's robustness and versatility. Those seeking to delve deeper into this topic are encouraged to explore the following resources: - [SVM: A Review](https://lnkd.in/dE8EP8fb) - [On Support Vector Methods for Function Approximation, Regression Estimation, and Signal Processing](https://lnkd.in/dyKubkxV) Additional Resources: [Optional] #STEM #Programming #MachineLearning #SupportVectorMachines #SVM #HingeLoss #Optimization #DataScience #Technology #AI #predictiveanalytics #datasciencecommunity Find this and all other slideshows for free on our website: https://lnkd.in/dbiSDKN9 #STEM #Programming #MachineLearning #SupportVectorMachines #SVM #HingeLoss #Optimization #DataScience #Technology #AI #predictiveanalytics #datasciencecommunity https://lnkd.in/dQay8vEb