লক্ষ্য করুন: এই এন্ট্রির অনুবাদ বর্তমানে মান পর্যালোচনার অধীনে রয়েছে, তাই কিছু বিষয়বস্তু সাময়িকভাবে শুধুমাত্র ইংরেজিতে প্রদর্শিত হচ্ছে।
এই এন্ট্রিটি এখনও আপনার ভাষায় অনুবাদ করা হয়নি, তাই নিচে মূল লেখাটি দেখানো হচ্ছে।
gradient descent
This term is a technical cornerstone of machine learning and mathematical optimization. It describes a process of iterative refinement where a model adjusts its parameters to find the lowest point of a cost function, akin to a hiker descending a mountain in thick fog by feeling the slope of the ground beneath their feet.
In professional contexts, it is often contrasted with "stochastic gradient descent" (SGD), which introduces randomness to speed up computation and avoid local minima. Because it is a specific algorithmic process, it is treated as a singular conceptual entity in technical discourse.
Meanings
Examples
The model uses gradient descent to minimize the loss function.
I wonder if a different learning rate would help the gradient descent converge faster.
We should implement stochastic gradient descent to handle the large dataset.
The professor explained how gradient descent finds the local minimum of a surface.
Is the gradient descent getting stuck in a saddle point?
The algorithm relies on gradient descent to update the weights of the neural network.
He realized that the gradient descent was diverging because the step size was too large.
Let us analyze the convergence properties of this specific gradient descent variant.