Juan C OlamendyHow to Align with Model’s Prediction with Real World OutcomesImagine you’re building a model.2d ago2d ago
Juan C OlamendyPractical ML: How to Find the Right Spot to Stop your Training Process to Save Money and TimeHave you ever wondered what’s the right moment to stop your training process?5d ago5d ago
Juan C OlamendyHow to Select the Right Features: A Practical GuideMachine learning (ML) models heavily rely on the quality and relevance of the features used to train them.Jun 18Jun 18
Juan C OlamendyReal World ML: The Hashing Trick: An Elegant Solution to Dynamic Category EncodingHave you ever encountered a situation where your ML model is not doing well in production due to encountering a category it hasn’t seen…Jun 17Jun 17
Juan C OlamendyReal-world ML: Contrastive Learning, The Power of Grasping the Data EssenceImagine trying to identify a rare disease with just a few x-ray images. The high cost of labeling and the scarcity of data make this task…Jun 12Jun 12
Juan C OlamendyReal World ML: Early Stopping in Deep Learning: A Comprehensive GuideHave you ever spent days training a deep learning model, only to find out it performs poorly on new data?Jun 10Jun 10
Juan C OlamendyUnlocking the Power of Vector Databases in Recommendation SystemsEver marveled at how platforms like Amazon, Netflix, or Spotify seem to predict your preferences with accuracy?Jun 5Jun 5
Juan C OlamendyPractical ML: Addressing Class ImbalanceEver wondered why your machine learning model is failing to detect rare but critical events?Jun 4Jun 4
Juan C OlamendyReal-World ML: Effective Labeling Strategies for Machine LearningHave you ever struggled with the time-consuming and resource-intensive task of labeling data for your machine learning projects?May 31May 31
Juan C OlamendyReal World ML — Understanding Batch Size. Train Faster and Better Deep Learning ModelsHave you ever spent days fine-tuning a deep learning models, only to see no difference on its performance?May 29May 29