THE DEEP LEARNING INTERVIEW GUIDE: The Ultimate Interview Guide for Students, Data Analyst, IT, Programmer and Data Scientist. Prepare yourself with this ... to win your next interview. Do it Yourself by Mills Aimee

Author:Mills, Aimee
Language: eng
Format: epub
Published: 2023-10-03T00:00:00+00:00

This challenge can be overcome while working with less data by experimenting with the neural network's parameters.

by expanding the amount of the training dataset. batch normalization is used.

By putting regularization in place

by making the network less complex

21. Batch Gradient Descent, describe.

Batch gradient descent is extremely slow when used with very large training sets since it requires computation (included in each step of gradient descent) over the full training set at each step. Batch Gradient Descent thus becomes quite computationally expensive. This is the best case for smooth or convex error manifolds. Additionally, Batch Gradient Descent scales well as the variety of characteristics increases.

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