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WHAT IS A MACHINE LEARNING MODEL?

Fueled by data, ML models are the mathematical engines of AI, expressions of
algorithms that find patterns and make predictions faster than a human can.
August 16, 2021 by Chris Parsons

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When you shop for a car, the first question is what model — a Honda Civic for
low-cost commuting, a Chevy Corvette for looking good and moving fast, or maybe
a Ford F-150 to tote heavy loads.

For the journey to AI, the most transformational technology of our time, the
engine you need is a machine learning model.


WHAT IS A MACHINE LEARNING MODEL?

A machine learning model is an expression of an algorithm that combs through
mountains of data to find patterns or make predictions. Fueled by data, machine
learning (ML) models are the mathematical engines of artificial intelligence.

For example, an ML model for computer vision might be able to identify cars and
pedestrians in a real-time video. One for natural language processing might
translate words and sentences.

Under the hood, a machine learning model is a mathematical representation of
objects and their relationships to each other. The objects can be anything from
“likes” on a social networking post to molecules in a lab experiment.


ML MODELS FOR EVERY PURPOSE

With no constraints on the objects that can become features in an ML model,
there’s no limit to the uses for AI. The combinations are infinite.

Data scientists have created whole families of machine learning models for
different uses, and more are in the works.


A BRIEF TAXONOMY OF ML MODELS

ML Model TypeUses Cases Linear regression/classificationPatterns in numeric
data, such as financial spreadsheets Graphic modelsFraud detection or sentiment
awareness Decision trees/Random forestsPredicting outcomes Deep learning neural
networksComputer vision, natural language processing and more

For instance, linear models use algebra to predict relationships between
variables in financial projections. Graphical models express as diagrams a
probability, such as whether a consumer will choose to buy a product. Borrowing
the metaphor of branches, some ML models take the form of decision trees or
groups of them called random forests.

In the Big Bang of AI in 2012, researchers found deep learning to be one of the
most successful techniques for finding patterns and making predictions. It uses
a kind of machine learning model called a neural network because it was inspired
by the patterns and functions of brain cells.


AN ML MODEL FOR THE MASSES

Deep learning took its name from the structure of its machine learning models.
They stack layer upon layer of features and their relationships, forming a
mathematical hero sandwich.

Thanks to their uncanny accuracy in finding patterns, two kinds of deep learning
models, described in a separate explainer, are appearing everywhere.

Convolutional neural networks (CNNs), often used in computer vision, act like
eyes in autonomous vehicles and can help spot diseases in medical imaging.
Recurrent neural networks and transformers (RNNs), tuned to analyze spoken and
written language, are the engines of Amazon’s Alexa, Google’s Assistant and
Apple’s Siri.

Deep learning neural networks got their name from their multilayered structure.


PSSSSST, PICK A PRETRAINED MODEL

Choosing the right family of models — like a CNN, RNN or transformer — is a
great beginning. But that’s just the start.

If you want to ride the Baja 500, you can modify a stock dune buggy with heavy
duty shocks and rugged tires, or you can shop for a vehicle built for that race.

In machine learning, that’s what’s called a pretrained model. It’s tuned on
large sets of training data that are similar to data in your use case. Data
relationships — called weights and biases — are optimized for the intended
application.

It takes an enormous dataset, a lot of AI expertise and significant compute
muscle to train a model. Savvy buyers shop for pretrained models to save time
and money.


WHO YA GONNA CALL?

When you’re shopping for a pretrained model, find a dealer you can trust.

NVIDIA puts its name behind an online library called the NGC catalog that’s
filled with vetted, pretrained models. They span the spectrum of AI jobs from
computer vision and conversational AI and more.

Users know what they’re getting because models in the catalog come with résumés.
They’re like the credentials of a prospective hire.

Model resumes show you the domain the model was trained for, the dataset that
trained it, and how it’s expected to perform. They provide transparency and
confidence you’re picking the right model for your use case.


MORE RESOURCES FOR ML MODELS

What’s more, NGC models are ready for transfer learning. That’s the one final
tune-up that torques models for the exact road conditions over which they’ll
ride — your application’s data.

NVIDIA even provides the wrench to tune your NGC model. It’s called TAO and you
can sign up for early access to it today.

To learn more, check out:

 * Our web page on pretrained models
 * A guide to the NGC catalog
 * Our web page on Tao and related tools
 * A developer blog on using pretrained models for computer vision to build a
   gesture recognition app
 * A talk from GTC 21 on transfer learning (free to view with registration)

Categories: Deep Learning | Explainer
Tags: Artificial Intelligence | Computer Vision | Data Science | Machine
Learning | NGC



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