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.

information from: https://www.ibm.com/topics/deep-learning