What are the main challenges of using gradient descent in machine learning?
The main challenges include local minima, slow convergence, and sensitivity to learning rates.
Can you explain what local minima means in this context?
Local minima refer to points where the algorithm gets stuck, thinking it's at the best solution, but it's not the global best.
How does the learning rate affect the performance of gradient descent?
A learning rate that's too high can cause overshooting, while one that's too low can lead to slow convergence.
Are there any techniques to overcome these challenges?
Yes, techniques like momentum, adaptive learning rates, and using different optimization algorithms can help.
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