Gradient descent, explained by rolling downhill
📰 Dev.to · I Want To Learn Programming
Learn gradient descent by visualizing a ball rolling downhill to minimize a function, a fundamental concept in machine learning
Action Steps
- Visualize a ball rolling downhill to understand the concept of gradient descent
- Implement gradient descent using three lines of code to minimize a parabola
- Apply gradient descent to fit a line to data and see how it optimizes the model
Who Needs to Know This
Data scientists and machine learning engineers can benefit from understanding gradient descent to improve model training and optimization
Key Insight
💡 Gradient descent is a simple yet powerful algorithm for optimizing models by iteratively moving in the direction of the negative gradient
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🏔️ Gradient descent explained: a ball rolling downhill to minimize a function! 📈
Full Article
Every model you have heard of is trained by one algorithm, gradient descent. The idea is a ball rolling downhill to the lowest point, and it is about three lines of code. Here it is, from minimizing a parabola to fitting a line to data.
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