E12. An Overview of Model Training

Episode 12: An Overview of Model TrainingWe've used the word "training" in every episode. Now, we break down exactly what it means. In this episode, we explore the step-by-step workflow of how a model actually "learns" from data.In this episode, we cover:The Core Concept: How "learning" is really just adjusting mathematical Weights and Biases to minimize a Loss Function.Model vs. Algorithm: Why these terms aren't interchangeable (Recipe vs. Meal).The 3 Paradigms Recap: A quick look at how Supervised, Unsupervised, and Reinforcement learning differ in their goals (Accuracy vs. Pattern Finding vs. Reward Maximization).The 8-Step Workflow: From Data Collection and Hyper-parameter Selection to Back-propagation and Optimization.Evaluation: Why we split data into Training, Validation, and Test sets to avoid the twin traps of Overfitting and Underfitting.Next Episode: We look at the tools of the trade in Machine Learning Libraries.