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

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.

This short read highlights some of the challenges companies face in machine learning projects. Number one is a problem we've seen in mathematical optimization projects as well.

TD3-BST: A Machine Learning Algorithm to Adjust the Strength of Regularization Dynamically Using Uncertainty Model https://buff.ly/4dquTaX

“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

In the field of causal inference (CI) and machine learning (ML), we can distinguish between: 1. Causal inference for machine learning and, 2. Machine learning for causal inference Example of ML for causal inference: meta-learners (s-learner, t-learner, x-learner) etc. Example of causal inference for ML: causal discovery, DAGs, etc. ML for Causal Inference: Applying ML techniques to estimate causal effects, identify causal relationships, and make counterfactual predictions. This is about using the power of ML to solve problems traditionally addressed by causal inference methods. Causal Inference for ML: Integrating causal reasoning into the ML workflow to enhance model interpretability, fairness, robustness, and generalization. This is about improving ML models by leveraging insights from causal inference.