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The Machine Learning platform stack
As shown in in the figure to the right, the machine
learning stack consists of:
The neural network (application) layer – this is the
data analysis model
The framework layer - provides the specialized
software neural networks run on
The math libraries layer - houses the math
routines the frameworks call
The operating system layer – choice of OS
The hardware platform layer -offers a number of
different accelerator options
The platform choices made at each of these layers can impact the performance and capabilities of the targeted
learning function. The following sections expand on important points that should be considered for these layers.
Neural Network layer
Neural networks are symbolic representations of the mathematical models created for a specific learned function (for
example, speech recognition). Neural networks come in many different shapes, sizes and functions, depending upon
both the type of data being ingested and the intended goal (output) of the learned function. The complexity of neural
network construction can vary by:
o Specific activation functions
o Number of activation layers
o Data set manipulation types: forward/backward propagation, convolutions, recurrence, LSTM, etc.
At the highest level, all neural networks break down input
features (such as the pixels in a photo) into multi-
dimensional arrays of data (tensors) and then pass them
through one or more layers of parameters (or weights) into
activation functions which can be represented as neurons
in the neural network.
Input tensors are multiplied by parameter tensors and
activation functions to yield a hypothesis that can be used
for a decision – for example, classification that an object
shown in a given picture is a cat or a dog. The size and
dimensions of the input features and the number of
activation layers is what determines how to handle the
necessary math operations in the hardware layer (i.e., you
may require multiple GPUs).
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