Note: As a courtesy to our followers, this blog article presents a synopsis of Microsoft’s article on tools available within Azure Machine Learning on July 7th, 2021.

Ever wonder what the difference is between deep learning vs machine learning and how they fit into the broader category of artificial intelligence?  Want to learn about deep learning solutions on Microsoft Azure Machine Learning such as fraud detection, voice and facial recognition, sentiment analysis and time series forecasting?

Deep learning is a subset of machine learning based on artificial neural networks. It’s called “deep” because the structure consists of many layers – from input layers thru hidden layers to output layers.

Machine learning enables machines to use the experience to improve at tasks. The “learning” process is based on the following steps.

1. Feed data into an algorithm.
2. Use this data to train a model.
3. Test and deploy the model.
4. Consume the deployed model to do an automated predictive task.

Deep Learning vs Machine Learning in Azure

There are two techniques. In machine learning, the algorithm needs to be told how to make an accurate prediction by consuming more information. In deep learning, the algorithm can learn how to make an accurate prediction through its own data processing, thanks to the artificial neural network structure. Here’s a comparison of the two techniques.

All machine learning Only deep learning
Number of data points Can use small amounts of data to make predictions. Needs to use large amounts of training data to make predictions.
Hardware dependencies Can work on low-end machines. It doesn’t need a large amount of computational power. Depends on high-end machines. It inherently does a large number of matrix multiplication operations. A GPU can efficiently optimize these operations.
Featurization process Requires features to be accurately identified and created by users. Learns high-level features from data and creates new features by itself.
Learning approach Divides the learning process into smaller steps. It then combines the results from each step into one output. Moves through the learning process by resolving the problem on an end-to-end basis.
Execution time Takes comparatively little time to train, ranging from a few seconds to a few hours. Usually takes a long time to train because a deep learning algorithm involves many layers.
Output The output is usually a numerical value, like a score or a classification. The output can have multiple formats, like a text, a score, or a sound.
Jim Eselgroth

Marc Vandeveer

Chief Innovation Officer


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