What is the difference between machine learning and deep learning?

Deep learning is better suited than machine learning for solving problems, because neural networks learn complex interactions among features in the data.

Deep Learning Algorithms

Q: What is deep learning?

Deep learning is the subfield of machine learning that uses neural networks, a family of algorithms modeled after the way that layers of neurons pass each other electrical signals in the brain.

What is Deep Learning?

Deep learning algorithms run data through several “layers” of neural network algorithms, each of which passes a simplified representation of the data to the next layer.

Most machine learning algorithms work well on datasets that have up to a few hundred features, or columns. However, an unstructured dataset, like one from an image, has such a large number of features that this process becomes cumbersome or completely unfeasible. A single 800-by-1000-pixel image in RGB color has 2.4 million features – far too many for traditional machine learning algorithms to handle.

Deep learning algorithms learn progressively more about the image as it goes through each neural network layer. Early layers learn how to detect low-level features like edges, and subsequent layers combine features from earlier layers into a more holistic representation. For example, a middle layer might identify edges to detect parts of an object in the photo such as a leg or a branch, while a deep layer will detect the full object such as a dog or a tree.

Why is Deep Learning Important?

The ability to process large numbers of features makes deep learning very powerful when dealing with unstructured data. However, deep learning algorithms can be overkill for less complex problems because they require access to a vast amount of data to be effective. For instance, ImageNet, the common benchmark for training deep learning models for comprehensive image recognition, has access to over 14 million images.

If the data is too simple or incomplete, it is very easy for a deep learning model to become overfitted and fail to generalize well to new data. As a result, deep learning models are not as effective as other techniques (such as boosted decision trees or linear models) for most practical business problems such as understanding customer churn, detecting fraudulent transactions and other cases with smaller datasets and fewer features. In certain cases like multiclass classification, deep learning can work for smaller, structured datasets.

Deep Learning + DataRobot

The DataRobot AI platform includes support for deep learning and neural networks with technologies like TensorFlow. Additionally, DataRobot employs several cutting-edge techniques that make deep learning more effective on smaller, less complex datasets. DataRobot develops dozens of different types of models, giving businesses the ability to compare deep learning with various other techniques and decide for themselves which models perform the best for the specific problem at hand.

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