Deep learning is a subfield of machine learning that uses algorithms inspired by the structure and functionality of the human brain. By clustering data in multi-layered artificial neural networks, computer systems can make more accurate predictions.
While a single artificial neural network can make an approximate prediction, the multiple layers involved enables the system to learn from larger amounts of data and thus refine and optimize its predictions. It is often used in AI technology for the likes of in-depth analytical tasks that would be practically impossible for a human to undertake to any degree of success, let alone with the incredible accuracy of deep learning.
It has already become a part of everyday life, as it is used in technology such as digital assistants, voice-command devices and even with the latest credit card fraud detection systems.
Let’s look closer at what deep learning is and how it works.
What Is Deep Learning or Machine Learning?
As a subfield of machine learning, deep learning is differentiated from classic machine learning due to the type of data it works with and the way it learns from that data.
The data machine learning works with often requires pre-processing into structured and labeled data, with specific features of the data defined and organized so it can be analyzed. Some machine learning can work with unstructured data, but it will still organize and structure the data itself before beginning any analysis.
Deep learning analyzes unstructured data, negating the need for pre-processing that is necessary for machine learning. The algorithms receive unstructured data such as text and images and then automates feature extraction, meaning that it extracts features automatically from signals in the text and images without the need for human intervention.
For a very simple example, if you were to give a machine learning system a series of pictures of tigers and giraffes for it to group each of the animals together with their own species, then a human would have to input the differences to look out for. So, the human would inform the system that tigers have stripes, or that giraffes have long necks. In contrast, deep learning would figure out the identifiable characteristics of each animal by itself.
Furthermore, deep learning would apply processes called gradient descent and backpropagation to adjust its ongoing analysis, enabling it to identify the animal in each image ever faster and more accurately each time.
Supervised, Unsupervised and Reinforcement Learning
Deep learning systems can employ different types of learning, often referred to as supervised learning, unsupervised learning, and reinforcement learning.
Supervised learning involves labeled datasets that the system categorizes, similarly to how machine learning works with human input, hence the name. Unsupervised learning relies on the system detecting patterns in the data without human input. It will then cluster the patterns together according to their distinguishing characteristics.
Reinforcement learning uses insight and feedback from its analytical processes to keep learning and improve its accuracy as the process goes along.
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