Deep learning is a branch of machine learning that uses artificial neural networks to model complex systems and relationships.
It’s become increasingly popular in recent years thanks to its ability to recognize patterns, interpret unstructured data, interpret images, audio signals and more. Deep learning algorithms are being used across many industries – from natural language processing and computer vision to medical diagnoses and financial forecasting.
While deep learning and artificial intelligence (AI) are closely related, they are not the same.
AI is a broader term that encompasses many different types of machine learning algorithms, such as those used in deep learning.
Deep learning falls under the umbrella of AI, since it uses neural networks to help machines better understand data and make decisions. However, AI has wider implications beyond deep learning, providing guidance for overall decision making capability.
The two main types of deep learning are supervised and unsupervised learning.
Supervised learning algorithms use labeled data to learn from.
Unsupervised learning, on the other hand, doesn’t require labeled data in order to learn.
In supervised learning, the algorithm is given a dataset with input-output relationships—or examples—that it can use to develop a model for predicting output values.
With unsupervised learning, the algorithm learns by uncovering patterns or insights in an unlabeled dataset.
Deep learning can help marketing professionals automate and accelerate time-consuming like customer segmentation For example, customer segmentation and improving targeting accuracy.
By working through large data sets quickly and without manual input, deep learning can help marketers make better data-backed decisions.
Deep learning help identify which variables have a greater influence on outcomes and how those variables interact with each other.
Deep learning algorithms can assess large amounts of data to determine the optimal customer segments for advertising campaigns, providing improved accuracy and higher returns on investment (ROI).
By using customer data to create personalized content, deep learning can deliver tailored experiences that help drive loyalty.
SAP Emarsys helps leading brands like PUMA, Pizza Hut, and Nike to harness the power of customer lifecycle marketing and make every customer interaction meaningful and impactful through personalized real-time engagements.
