Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Built In brought this interesting subject to us in their article, “What Is Deep Learning and How Does It Work?“
Deep learning is an important element of data science, which includes statistics and predictive modeling. It is extremely beneficial to data scientists who are tasked with collecting, analyzing, and interpreting large amounts of data, because deep learning makes this process faster and easier.
Computer programs that use deep learning go through much the same process as a child learns to identify a toy. Each algorithm in the hierarchy applies a nonlinear transformation to its input and uses what it learns to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the label deep.
Most organizations have little knowledge on how AI technologies make the decisions they do, and as a result, how the results are being applied in the various fields. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.
Melody K. Smith
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