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Dipayon Paul

Dipayon Paul

Preventing Overfitting: The Role of Training, Validation, and Test Sets

In machine learning, the ultimate goal is to build models that generalize well to unseen data. We don't just want a model that performs well on the data it was trained on; we want a model that can accurately predict outcomes on new, real-world data. To achieve this,

Supervised, Unsupervised, and Reinforcement Learning: Understand the Differences and Key Applications

Machine learning (ML) is a powerful field that enables computers to learn from data without explicit programming. Within ML, there are three fundamental learning paradigms: supervised learning, unsupervised learning, and reinforcement learning. Understanding the distinctions between these approaches is crucial for choosing the right technique for a given problem and

From Zero to AI Hero: Mastering Machine Learning and Deep Learning

From Zero to AI Hero: Mastering Machine Learning and Deep Learning

Okay, let's outline a learning path for Machine Learning and Deep Learning. This will be a comprehensive roadmap, and you can tailor it to your current knowledge and goals. I'll break it down into stages, covering foundational concepts, core skills, and advanced topics. I. Foundational Concepts