In previous section, we studied about Neural Networks Conclusion

In this post we will discuss the math behind a few steps of Neural Network algorithms.

- We update the weights backwards by iteratively calculating the error.
- The formula for weights updating is done using gradient descent method or delta rule also known as Widrow-Hoff rule.
- First we calculate the weight corrections for the output layer then we take care of hidden layers.
- \(W_(jk) = W_(jk) + \Delta W_(jk)\)
- where \(\Delta W_(jk) = \eta . y_j \delta_k\)
- \(\eta\) is the learning parameter
- \(\delta_k = y_k (1- y_k) * Err\) (for hidden layers \(\delta_k = y_k (1- y_k) * w_j * Err )\)
- Err = Expected output-Actual output

- The weight corrections is calculated based on the error function.
- The new weights are chosen in such way that the final error in that network is minimized.

- Let’s consider a simple example to understand the weight updating using the delta rule.

- If we are building a simple logistic regression line. We would like to find the weights using weight update rule.
- \(Y= \frac{1}{(1+e^(-wx))}\) is the equation.
- We are searching for the optimal w for our data

- Let w be 1
- \(Y=\frac{1}{(1+e^(-x))}\) is the initial equation
- The error in our initial step is 3.59
- To reduce the error we will add a delta to w and make it 1.5

- Now w is 1.5 (blue line)
- \(Y=\frac{1}{(1+e^(-1.5x))}\) the updated equation
- With the updated weight, the error is 1.57
- We can further reduce the error by increasing w by delta

- If we repeat the same process of adding delta and updating weights, we can finally end up with minimum error.
- The weight at that final step is the optimal weight.
- In this example the weight is 8, and the error is 0.
- \(Y=\frac{1}{(1+e^(-8x))}\) is the final equation.
- In this example, we manually changed the weights to reduce the error. This is just for intuition, manual updating is not feasible for complex optimization problems.
- In gradient descent is a scientific optimization method. We update the weights by calculating gradient of the function.

- Gradient descent is one of the famous ways to calculate the local minimum.
- By Changing the weights we are moving towards the minimum value of the error function. The weights are changed by taking steps in the negative direction of the function gradient(derivative).

- We changed the weights did it reduce the overall error?
- Lets calculate the error with new weights and see the change

- With our initial set of weights the overall error was 0.7137,Y Actual is 0, Y Predicted is 0.7137 error =0.7137.
- The new weights give us a predicted value of 0.70655.
- In one iteration, we reduced the error from 0.7137 to 0.70655.
- The error is reduced by 1%. Repeat the same process with multiple epochs and training examples, we can reduce the error further.

- “ROC curve” by Masato8686819 – Own work. Licensed under CC BY-SA 3.0 via Wikimedia Commons – https://commons.wikimedia.org/wiki/File:ROC_curve.svg#/media/File:ROC_curve.svg
- “Curvas”??????UPO649 1112 prodgom – ?????????????????????????????????????????? – https://commons.wikimedia.org/wiki/File:Curvas.png#/media/File:Curvas.png??????CC BY-SA 3.0??????
- http://www.autonlab.org/tutorials/neural.html
- “Gradient ascent (surface)”. Licensed under Public Domain via Commons – https://commons.wikimedia.org/wiki/File:Gradient_ascent_(surface).png#/media/File:Gradient_ascent_(surface).png
- “Gradient descent method” by ?????????? ???????? – ???????????????????????? ????????????????????, ???????????????? ??????????????. Licensed under CC BY-SA 3.0 via Wikimedia Commons – https://commons.wikimedia.org/wiki/File:Gradient_descent_method.png#/media/File:Gradient_descent_method.png
- Lecture 7 :Artificial neural networks: Supervised learning: Negnevitsky, Person Education 2005
- Gradient descent can find the local minimum instead of the global minimum By I, KSmrq
- “Neuron”. Licensed under CC BY-SA 3.0 via Wikimedia Commons – https://commons.wikimedia.org/wiki/File:Neuron.svg#/media/File:Neuron.svg
- “Neural signaling-human brain” by 7mike5000 – Gif created from Inside the Brain: Unraveling the Mystery of Alzheimer’s Disease, an educational film by the National Institute on Aging.. Licensed under CC BY-SA 3.0 via Wikimedia Commons – https://commons.wikimedia.org/wiki/File:Neural_signaling-human_brain.gif#/media/File:Neural_signaling-human_brain.gif

In next section, we will be studying about Introduction to SVM