Another Day another lection in neuronal Networks.

today we are going on with the third part.

You can find all previous posts in this list:

Part	Headline	Steemit Link:
I	Introduction	https://steemit.com/neuronalnetwork/@haggy2k3/neuronal-networks-part-1-introduction
II	Units	https://steemit.com/science/@haggy2k3/neuronal-networks-part-ii-units
III	Connections	Current Document
IV	Inputs	https://steemit.com/science/@haggy2k3/neuronal-networks-part-iv-inputs
V	Activities and Outputs
VI	Training and Test
VII	Metrics
VIII	Learning Rules
IX	Types of NN
X	Features of NN
XI	Use Cases
XII	Bonus: Getting started with Membrain

Connections between units

Units are connected to each other by edges. The strength of the connection
between two neurons is expressed by a weight. The greater the absolute value of
the weight, the greater the influence of one unit on another unit.

A positive weight indicates that one neuron exerts an excitatory influence on
another neuron.

A negative weight means that the influence is inhibitory.

A weight of zero indicates that one neuron currently has no influence on another
neuron.

The knowledge of a neural network is stored in its weights.

Learning in neural networks is usually defined as weight changes between the
units. How exactly the weight change is made depends on the learning rule used.

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Haggy2k3