100% Off Coupon - Machine Learning and Deep Learning Optimizers Implementation

What you’ll learn

Python Implementation of Gradient Descent Optimizer to Train Single Variable Linear Regression Model.
Python Implementation of Gradient Descent Optimizer to Train Multi Variable Linear Regression Model.
Python Implementation of Stochastic Gradient Descent (SGD) for Single Variable LR.
Python Implementation of SGD for Multi-Variable LR.
Python Implementation of Mini-Batch GD for Single and Multi-Variable LR.
Generalization of Mini-Batch to Work as SGD and Batch GD.
Generalization of Multi-Variable LR to Work for Single Variable LR.
Numerical Optimization Problem Definition.
Model Parameters Initialization.
Calculating MSE Cost Function as Vector Norm.
How to Calculate the Gradient?
Model Parameters Update.
Iterate Till Achieving the Optimum Parameter Values.
Gradient Stop Condition.
Model Performance Evaluation.
How to Obtain Better Model Performance?
Plotting of Learning Curves.
Cost Convergence Check.
Make Your Implementation as a Function.
Multivariable Linear Regression Problem Definition.
Going from Single Variable to Multivariable Linear Regression.
Gradient Descent Optimizer Vectorize Implementation.
Why We Use Vectorize Implementation.
Generalization of the Implementation to Work for Single and Multivariable Linear Regression.
Data Preparation for Vectorize Implementation.
Dimensions Problem and How to Deal With it.
Make Your Implementation of Vectorize Implementation as a Function.
Try Different Combinations of Hyperparameters.

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