Gradient Descent is an optimization algorithm crucial for machine learning, used to minimize a model's cost function. It adjusts parameters based on the cost function's gradient, with the learning rate dictating step size. Variants like Batch, Stochastic, and Mini-batch Gradient Descent cater to different data sizes and computational needs, proving vital from linear regression to deep neural network training.
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Gradient Descent minimizes the cost function, which measures the difference between a model's predictions and the actual data
Parameter Adjustment
Gradient Descent iteratively adjusts a model's parameters to reduce the cost function's value
Learning Rate
The learning rate, represented by alpha (α), determines the size of the parameter adjustments in Gradient Descent
Gradient Descent continues until the cost function no longer decreases significantly, indicating convergence to the minimum
Gradient Descent calculates the gradient of the cost function with respect to each parameter
The algorithm updates the model's parameters in the direction that reduces the cost function (the negative gradient direction)
The learning rate determines the size of the parameter adjustments in Gradient Descent
Batch Gradient Descent uses the entire training dataset for each update, providing a stable but computationally intensive process
SGD uses a single data point for each update, resulting in faster but less consistent updates
Mini-batch Gradient Descent uses a small, randomly selected subset of the data for each update, balancing computational load and convergence stability
Gradient Descent is used in linear regression to determine the optimal coefficients for the line that best fits the data
Gradient Descent is also effective in tackling complex, non-linear problems, such as training deep neural networks
Gradient Descent has a wide range of applications, from simple linear regression to training intricate neural networks, making it a cornerstone algorithm in machine learning
00
______ Descent is a key optimization algorithm in ______ learning for minimizing a model's cost function.
Gradient
machine
01
If the learning rate is too high, the algorithm might ______ the minimum, while a rate too low could lead to a long ______ time.
overshoot
convergence
02
Gradient Descent: Cost Function Role
Cost function guides parameter adjustments; Gradient Descent minimizes its value for optimization.
