Review:
Batch Gradient Descent
overall review score: 4.2
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score is between 0 and 5
Batch gradient descent is an optimization algorithm used in machine learning to minimize the cost function by iteratively updating the parameters of a model based on the gradients of the entire dataset.
Key Features
- Iterative optimization algorithm
- Utilizes gradients of the entire dataset
- Updates model parameters to minimize cost function
Pros
- Efficient for small to medium-sized datasets
- Converges to a global minimum with proper learning rate tuning
- Can be parallelized for faster computation
Cons
- Computationally expensive for very large datasets
- May converge slowly if learning rate is not properly tuned
- Suffers from high variance in noisy data