What are the sequential learning types to optimize the solution?

Question

BlackTom AI · Accepted Answer

The question asks about the sequential learning types used to optimize the solution. Here are the options considered: 
Option 1: Gradient Descent. This is a fundamental optimization method where the parameters are updated iteratively in the direction that reduces the loss function the most. It treats the entire dataset at once (batch) in its vanilla form, and in broader contexts serves as the baseline family of optimization techniques for learning models. Its role in sequential or iterative improvement is as the general approach from which variants are derived.
Option 2: Stochastic Gradient Descent. This is a variant of gradient descent designed for efficiency on large datasets. Instead of computing the gradient over the full dataset, it updates parameters using individual (or small batch) samples, producing more frequent updates and often faster convergence in practice for large-scale problems. It embodies the sequential/iterative nature of updating parameters as data points flow in, which aligns with the idea of sequential learning to optimize solutions over time.
Now, evaluating the choices helps clarify their relation to sequential optimization: 
- Gradient Descent (Option 1) embodies the core idea of iterative improvement, but depending on implementation it can be batch-based. When used in a sequential or iterative context, it aligns with the general principle of gradually adjusting parameters to minimize loss.
- Stochastic Gradient Descent (Option 2) explicitly captures the sequential, incremental update process, as each (or mini-batch) example contributes to a new update step and thus is a quintessential sequential optimization approach for large datasets.
If the aim is to identify learning types that optimize solutions through sequential updates, both options describe important, widely used methods in the optimization toolbox. The distinction lies in how data is processed at each update and how frequently updates occur, with SGD emphasizing frequent, smaller updates and Gradient Descent often representing a full-batch, one-shot or less frequent update depending on the variant used.
In summary, why these options fit into the concept: Gradient Descent provides the foundational update rule for reducing loss, while Stochastic Gradient Descent extends that rule to a sequential data-processing regime, enabling quicker iterations on large-scale data. Both are connected to the idea of sequential improvement in learning systems.

Machine Learning: Fundamentals and Applications Quiz 1 - Linear Regression

What are the sequential learning types to optimize the solution?

View Explanation

Log in for full answers

Similar Questions

The simplex method can outperform gradient descent when the loss function has many local minima.

You are optimising a complex function with many local minima and maxima. Which of the following are likely to help you find the global minimum value?

Please select all the statements about Newton's method and gradient descent that are correct.

In a consumer society, many adults channel creativity into buying things

Economic stress and unpredictable times have resulted in a booming industry for self-help products

People born without creativity never can develop it

A product has a selling price of $20, a contribution margin ratio of 40% and fixed cost of $120,000. To make a profit of $30,000. The number of units that must be sold is: Type the number without $ and a comma. Eg: 20000

Which of the following statement regarding cost is correct:

More Practical Tools for Students Powered by AI Study Helper

Homework AI Solver

Stylized AI Paper Writer

Plagiarism Checker Assistant

Citation AI Academic Writing Tool

In-Class Translation Assistant

AI Note Generator

AI Quiz Answers

Past Exam Questions from University Test Bank

Smart Practice Assistant

Adaptive Practice

Making Your Study Simpler