The document discusses the basic structure and function of artificial neurons. A single neuron receives multiple input signals, multiplies each by a weight, and outputs a single signal. Networks of neurons connected by synapses can learn relationships in data through processes like backpropagation. The output of a neuron can provide a prediction for problems like classification or regression.

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Image Source: www.austincc.edu

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Image Source: Wikipedia

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Image Source: Wikipedia
Neuron
Axon
Dendrites

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Image Source: Wikipedia

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neuron
Node

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Input signal 1
Input signal 2
Input signal m
neuron

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Input signal 1
Input signal 2
Input signal m
Output signalneuron

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neuron
Input value 1
Input value 2
Input value m
X1
X2
Xm
Output signal
Synapse

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neuron
Input value 1
Input value 2
Input value m
X1
X2
Xm
y Output value
Synapse

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neuron
X1
X2
Xm
Output value
Independent
variable 1
Independent
variable 2
Independent
variable m
Input value 1
Input value 2
Input value m
y
Standardize

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Efficient BackProp
By Yann LeCun et al. (1998)
Link:
http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf
Additional Reading:

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neuron
Input value 1
Input value 2
Input value m
Output value
X1
X2
Xm
y Output value
Can be:
• Continuous (price)
• Binary (will exit yes/no)
• Categorical
Independent
variable 1
Independent
variable 2
Independent
variable m

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neuron
Input value 1
Input value 2
Input value m
Output value 1
Output value 2
Output value p
X1
X2
Xm
yy2
y1
y3
Independent
variable 1
Independent
variable 2
Independent
variable m

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neuron
Single Observation
Same observation
X1
X2
Xm
y
Input value 1
Input value 2
Input value m
Output value
Independent
variable 1
Independent
variable 2
Independent
variable m
Single Observation

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neuron
Input value 1
Input value 2
Input value m
Output value
X1
X2
Xm
y
w1
w2
wm

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?neuron
Input value 1
Input value 2
Input value m
y
X1
X2
Xm
w1
w2
wm
Output value

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Input value 1
Input value 2
Input value m
y
1st step:
X1
X2
Xm
w1
w2
wm
Output value

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Input value 1
Input value 2
Input value m
y
2nd step:
X1
X2
Xm
w1
w2
wm
Output value

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Input value 1
Input value 2
Input value m
y
2nd step:
X1
X2
Xm
w1
w2
wm
Output value
3rd step