๐จ๐ป๐ฑ๐ฒ๐ฟ๐๐๐ฎ๐ป๐ฑ๐ถ๐ป๐ด ๐๐ต๐ฒ ๐๐บ๐ฝ๐ฎ๐ฐ๐ ๐ผ๐ณ ๐๐ต๐ผ๐ผ๐๐ถ๐ป๐ด ๐๐ต๐ฒ ๐ฅ๐ถ๐ด๐ต๐ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ฅ๐ฎ๐๐ฒ
In machine learning, the ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ฟ๐ฎ๐๐ฒ is a crucial ๐ต๐๐ฝ๐ฒ๐ฟ๐ฝ๐ฎ๐ฟ๐ฎ๐บ๐ฒ๐๐ฒ๐ฟ that directly affects model performance and convergence. However, many practitioners select it arbitrarily without fully optimizing it, often overlooking its impact on learning dynamics.
To better understand how the learning rate influences model training, particularly through gradient descent, visualization is a powerful tool. Here's how you can deepen your understanding:
๐น ๐ฅ๐ฒ๐ฐ๐ผ๐บ๐บ๐ฒ๐ป๐ฑ๐ฒ๐ฑ ๐๐ถ๐ฑ๐ฒ๐ผ๐: by Pritam Kudale
โข Loss function and Gradient descent:
โข Concept of linear regression and R2 score:
โข Hyoeroarameter Tuning:
๐ป ๐๐ ๐ฝ๐น๐ผ๐ฟ๐ฒ ๐๐ต๐ถ๐ ๐ฝ๐ฟ๐ฎ๐ฐ๐๐ถ๐ฐ๐ฎ๐น ๐ฑ๐ฒ๐บ๐ผ๐ป๐๐๐ฟ๐ฎ๐๐ถ๐ผ๐ป:
Learning Rate Visualization in Linear Regression: https://github.com/pritkudale/Code_for_LinkedIn/blob/main/learning_Rate_LR.ipynb
#MachineLearning #LinearRegression #LearningRate #GradientDescent #AIInsights #DataScience