Supervised vs. Deep Learning in Your AI Platform

What is the difference between supervised vs. deep learning?

The type of data you have will determine what AI/machine learning algorithms will best work for you – unless you have a tool that automatically adjusts to the best model for you. 

At the base level, both Supervised Machine Learning and Deep Learning are progressively deeper subsets of AI. But there are differences in how they work. What one will work best for your business and your needs will vary by the data you have. 

Supervised Machine Learning was the first of these two solutions and is defined by its use of labeled datasets to train algorithms to classify data or predict outcomes accurately. 

Deep Learning is the next level of machine learning, a more specialized subset that employs hierarchical layers of artificial neural networks that perform better on large, complicated data sets than simple neural networks. 

Deep Learning algorithms may achieve superior performance for industries adopting data lake technology, like Snowflake, compared with the well-labeled datasets that have been required for supervised machine learning to date.

Squark is an end-to-end automated solution that is powerful, flexible, and nimble to adjust to whatever data systems you currently use. Your data is automatically made better through automated data cleaning, preparation, and feature engineering, which get applied to every project without the user needing to take any additional steps. Thousands of models are run against the data in minutes before determining the best model for your data. The predictive results (and supporting insights) are laid out in easy-to-understand tables with visuals.

The takeaway: The type of data you have and its storage may impact the type of AI machine learning solution you’ll want when considered supervised vs. deep learning. You’ll either need to hire specialists per your needs, find a specific tool, or work with a tool, like Squark, that can determine what type of data you have and adjust the models accordingly.