Gradient Descent & Hyperparameters
Description
Based on the “Machine Learning ” crash course from Google for Developers: https://developers.google.com/machine-learning/crash-course
What drives a machine learning model to learn? In this episode, we explore gradient descent, the optimization engine behind linear regression, and the crucial role of hyperparameters like learning rate, batch size, and epochs. Understand how models reduce error step by step, and why tuning hyperparameters can make or break performance. Whether you're a beginner or reviewing the basics, this episode brings clarity with real-world analogies and practical takeaways.
Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.
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