2. What is a Neural Network?
- Neural Networks are computational systems designed to mimic the
human brain (in a loose sense)
- They try to approach and solve problems in the way we do
- E.g. Telling whether a picture of a bird is a picture of a bird or not, etc.
- They are shown many examples, and learn to make conclusions based off
what they’ve learned
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3. What can a neural network do?
Pattern Recognition
Neural networks are
used to identify objects
in images (faces,
animals, things, etc.), to
understand spoken
sentences (Alexa, Siri,
etc.), and in many
similar areas
They are central to how
self-driving cars work,
and are used in video
game AI development
Control Time Series Analysis
Neural networks are
used extensively to
predict the stock
market, the weather,
and a host of other
complex systems.
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4. What does a neural network look like?
- In general, they consist of:
- Input layer, where information is fed
in
- ‘Hidden’ layer(s), where units (or
‘neurons’) dynamically process the
inputs
- Output layer, where predictions or
conclusions are returned
- There are many variations of
network architecture
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5. Neural Network Development
Infrastructure
Build the Network
Write the code that will
govern the processes
and architecture of the
neural network (using
TensorFlow, Theano,
etc.). Design the
architecture for the
problem you want to
solve.
Training
Let It Learn
Collect data on what
you’re trying to identify
or predict, and use your
favourite machine
learning algorithm to let
the neurons learn the
best weights. Test the
model(s).
Application
Make Predictions
Use the models with
data you want
predictions or
conclusions for.
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