Discover some of the reasons why some algorithms are much slower than others while focusing on fitting a large number of coefficients.
- [Instructor] The second reason…that some algorithms are much slower than others…is some algorithms just perform more calculations.…Let's take a look.…This is an artificial neural network…that's been built on the same data set.…All we're trying to do is predict whether…or not an expectant mom will have a low birth weight baby.…Now, artificial neural networks get quite technical,…but take a look.…I just want you to attend to the very large number…of lines.…If we were using a statistically-based approach,…we've only got three variables here,…weight of the mom, uterine irritability, and hypertension.…
A statistical approach would only do four calculations,…one per variable and what's called the constant,…but the point is there would only be four.…But look at all these lines, many, many more,…so the neural net is doing many more calculations.…That makes it more accurate, but it makes it slower.…Over here, we've got six times five lines,…and yes, indeed, there's a relationship between the number…of shapes on the left and the number…
Note: This course is software agnostic. The emphasis is on strategy and planning. Examples, calculations, and software results shown are for training purposes only.
- Evaluating the proper amount of data
- Assessing data quality and quantity
- Seasonality and time alignment
- Data preparation challenges
- Data modeling challenges
- Scoring machine-learning models
- Deploying models and adjusting data prep and scoring
- Monitoring and maintenance
Skill Level Beginner
Machine Learning and AI Foundations: Recommendationswith Adam Geitgey58m 7s Intermediate
Deploying Scalable Machine Learning for Data Sciencewith Dan Sullivan1h 43m Intermediate
Defining terms1m 48s
1. The Phases of a Machine Learning Project
2. Designing a Machine Learning Dataset
3. Data Prep Challenges
4. Modeling Challenges
7. Monitoring and Maintenance
Next steps1m 1s
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