Deep Learning

What is deep learning?

Deep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain. Some form of deep learning powers most of the artificial intelligence (AI) applications in our lives today.

The chief difference between deep learning and machine learning is the structure of the underlying neural network architecture. “Nondeep,” traditional machine learning models use simple neural networks with one or two computational layers. Deep learning models use three or more layers, but typically hundreds or thousands of layers to train the models.

While supervised learning models require structured, labeled input data to make accurate outputs, deep learning models can use unsupervised learning. With unsupervised learning, deep learning models can extract the characteristics, features and relationships they need to make accurate outputs from raw, unstructured data. Additionally, these models can even evaluate and refine their outputs for increased precision.

Deep learning is an aspect of data science that drives many applications and services that improve automation, performing analytical and physical tasks without human intervention. This enables many everyday products and services, such as digital assistants, voice-enabled TV remotes, credit card fraud detection, self-driving cars and generative AI.

How deep learning works

Neural networks, or artificial neural networks, attempt to mimic the human brain through a combination of data inputs, weights and bias, all acting as silicon neurons. These elements work together to accurately recognize, classify and describe objects within the data.

Deep neural networks consist of multiple layers of interconnected nodes, each building on the previous layer to refine and optimize the prediction or categorization. This progression of computations through the network is called forward propagation. The input and output layers of a deep neural network are called visible layers. The input layer is where the deep learning model ingests the data for processing, and the output layer is where the final prediction or classification is made.

Another process called backpropagation uses algorithms, such as gradient descent, to calculate errors in predictions, and then adjusts the weights and biases of the function by moving backwards through the layers to train the model. Together, forward propagation and backpropagation enable a neural network to make predictions and correct for any errors. Over time, the algorithm becomes gradually more accurate.

Deep learning requires a tremendous amount of computing power. High-performance graphical processing units (GPUs) are ideal because they can handle a large volume of calculations in multiple cores with copious memory available. Distributed cloud computing might also assist. This level of computing power is necessary to train deep algorithms through deep learning. However, managing multiple GPUs on premises can create a large demand on internal resources and be incredibly costly to scale. For software requirements, most deep learning apps are coded with one of these three learning frameworks: JAX, PyTorch or TensorFlow.