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.
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.
For more deep learning use cases, check out: Deep learning vs. machine learning – Azure Machine Learning | Microsoft Docs.
For a cheat sheet to help choose the right algorithm, check out: Machine Learning Algorithm Cheat Sheet – designer – Azure Machine Learning | Microsoft Docs
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