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5 Deep Learning using iAbra stack on Dell EMC PowerEdge Servers with Intel technology
1 Overview of Deep Learning
Deep learning consists of two phases: Training and inference. As illustrated in Figure 1, training involves
learning a neural network model from a given training dataset over a certain number of training iterations and
loss function [1]. The output of this phase, the learned model, is then used in the inference phase to speculate
on new data.
The major difference between training and inference is training employs forward propagation and backward
propagation (two classes of the deep learning process) whereas inference mostly consists of forward
propagation [2]. To generate models with good accuracy, the training phase involves several training
iterations and substantial training data samples, thus requiring many-core CPUs or GPUs to accelerate
performance.
Deep Learning phases.