TensorFlow
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Units of data are called tensors. Tensors are a set of primative values (represented by an array/vector)
 rank is the dimensionality of a tensor (array)
 tensorflow is modelled with a computaitonal graph
 series of operations represented as a graph
 simplist node is a constant, taking zero inputs, and outputting a stored constant
 this function abstractly represents a constant, but isn't one until it runs in a session

tf.Session.run(tensor1)
would return the function output of tensor1

tf.Variable([...], tf.float32 )
constructs a variable with an initial value and type
 variables are not initalized in a tf program until
tf.global_variables_initializer()
 operations are nodes,
tf.add
, used to combine tensors

tf.placeholder(tf.float32)
are a promises to accept external input values (for user parameterization)

tf.assign(myVar, myVal)
can be passed to the tf session to reassign variable values
MNIST is a computer vision dataset, and a simple task is to use a simple model called softmax regression  source
 onehot vectors are 0 in most dimensions, and 1 in a single dimension
 for MNIST digits, [0,0,0,1,0,0,0,0,0,0] represents the literal number 3
 goal is to assign a probability to each possible output (0..9), given an image input
 softmax regression is used to assign probabilities to each output, acting as a simple model itself or a final layer in a more complex system