An executive’s guide to AI
Share
Artificial intelligence
Machinelearning
Deeplearning
Deeplearning
Deep learning
Major models
Recurrentneural network
Convolutionalneural network
What it is
When to use it
How it works
Business use cases
Inputlayer
Hiddenlayer
Outputlayer
Context nodes
A multilayered neural network that can store information in context nodes, allowing it to learn data sequences and output a number or another sequence
An executive’s guide to AI
Share
Artificial intelligence
Machinelearning
Deeplearning
Deeplearning
Deep learning
Major models
Recurrentneural network
Convolutionalneural network
What it is
When to use it
How it works
Business use cases
Feature extraction and mapping
Input
Output
Image classification: Human
A multilayered neural network with a special architecture designed to extract increasingly complex features of the data at each layer to determine the output
An executive’s guide to AI
Share
Artificial intelligence
Machinelearning
Deeplearning
Deeplearning
Deep learning
Major models
Deep learning is a type of machine learning that can process a wider range of data resources, requires less data preprocessing by humans, and can often produce more accurate results than traditional machine-learning approaches (although it requires a larger amount of data to do so). In deep learning, interconnected layers of software-based calculators known as “neurons” form a neural network. The network can ingest vast amounts of input data and process them through multiple layers that learn increasingly complex features of the data at each layer.
The network can then make a determination about the data, learn if its determination is correct, and use what it has learned to make determinations about new data. For example, once it learns what an object looks like, it can recognize the object in a new image.
The network can then make a determination about the data, learn if its determination is correct, and use what it has learned to make determinations about new data. For example, once it learns what an object looks like, it can recognize the object in a new image.
Deep learning can often outperform traditional methods
% reduction in error rate achieved by deep learning vs traditional methods
Image classification
41%
Facial recognition
27%
Voice recognition
25%
Next: Major models