Neural Networks w/ JAVA - Tutorial 05

00:06 step #0 randomly initialize weights for n inputs

00:28 objective is to determine what weights would lead to

'Target Result = Result' for all vectors in training data

00:42 step#1 calculate weighted sum, step#2 apply

activation function, step#3 determine error, step#4 adjust weights

01:17 demo app. with various number of inputs

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'Neural Networks w/ JAVA - Tutorial 05'

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Neural Networks w/ JAVA - Tutorial 04

00:06 have 3 inputs + a bias and need to obtain equation of a plane separating the 0s and 1s

00:35 step #0 randomly initialize weights w0, w1, w2, and w3

00:45 step #1 calculate weighted sum

01:00 step #2 apply activation function

01:18 step #3 determine error

01:54 'learning rate' is the rate at which the neural network learns (ranges from 0 to 1)

01:27 step #4 adjust weights

02:31 objective here is to determine what weights would

lead to 'Target Result' = 'Result' for all vectors in training data

02:02 repeat steps 1 to 4 until error = 0

03:32 demo prebuilt version of the app.

05:09 code the application

10:46 test run completed application

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'Neural Networks w/ JAVA - Tutorial 04'

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Neural Networks w/ JAVA - Tutorial 03

 

00:06 change THRESHOLD to 0.0 + run neural networks tutorial 02

00:24 unsuccessfull in looping through additional epochs

until error = 0 for all training vectors in final epoch

02:20 explain adding a bias

02:50 objective here is to determine what weights would

lead to 'Target Result' = 'Result' for all vectors in training data

03:40 code the application

14:45 test run completed application

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'Neural Networks w/ JAVA - Tutorial 03'

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Neural Networks w/ JAVA - Tutorial 02

 

00:06 obtain equation of line separating the 0s and 1s

00:32 step #0 randomly initialize weights

00:39 step #1 calculate weighted sum

00:50 step #2 apply activation function

01:10 step #3 determine error

01:19 step #4 adjust weights

01:39 repeat steps 1 to 4 until error = 0

01:57 objective here is to determine what weights would

lead to 'Target Result' = 'Result' for all vectors in training data

02:13 set weighted sum equal to the threshold

02:47 demo a prebuilt version of the app.

03:25 code the application

16:00 test run completed application

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'Neural Networks w/ JAVA - Tutorial 02'

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Neural Networks w/ JAVA (Tutorial 06) - Solve XOR w/ Hill Climbing

 

00:06 try running 'neural network tutorial 03'

w/ XOR training data

00:56 not possible to have one line separating

the 0s and 1s for XOR trainig data

01:33 demo prebuilt version of the application

02:32 use a neural network with 5 neurons

(2 in input layer, 2 in hidden layer, and 1 in output layer),

and where each neuron has 2 inputs and a threshold

03:16 proceed when 'adjusted error' is smaller or equal to current error

04:38 code the application

22:38 test run completed application

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'Neural Networks w/ JAVA (Tutorial 06) - Solve XOR w/ Hill Climbing'

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Neural Networks w/ JAVA (Solve XOR w/ Simulated Annealing) - Tutorial 07

 

00:06 demo prebuilt version of the application

00:42 use a neural network with 5 neurons

(2 in input layer, 2 in hidden layer, and 1 in output layer),

and where each neuron has 2 inputs and a threshold

01:20 proceed when 'adjusted error' is smaller or equal to 'current error'

01:39 proceed if prob bigger than 'random #' when 'adjusted error'

is bigger than 'current error'

03:00 as the temperature gets lower and lower we proceed less and less often

03:44 code the application

24:00 test run completed application

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'Neural Networks w/ JAVA

(Solve XOR w/ Simulated Annealing) - Tutorial 07'

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