## Octave Activation Key ((FULL))

Octave Activation Key ->>> __https://blltly.com/2tptcH__

Octave Activation Key ((FULL))

On a saxophone, the octave key is positioned next to the left-hand thumb rest. Pressing the octave key opens the top tone hole in the neck of the saxophone. Alternatively, whenever the G key is fingered, the top tone hole closes and a small tone hole is opened near the top of the body.

The modern oboe has two octave keys, sometimes three, often interconnected, the one for E5 to G#5 near the left thumb, and the one for A5 to C6 to the right of and above the front keys, depressed by the edge of the left index finger. Oboes are now available with automatic octaves. This involves extra keywork that frees the player from having to bother with an octave key at all. The bassoon has similar keys used by the left thumb, but these are usually only depressed at the attack of notes, or "flicked".

When called with no extra input arguments, it returns the Octave license,otherwise the first input defines the operation mode and must be one ofthe following strings: inuse, test, and checkout.The optional feature argument can either be "octave" (core),or an Octave package.

This is the third part in my series on Deep Learning from first principles in Python, R and Octave. In the first part Deep Learning from first principles in Python, R and Octave-Part 1, I implemented logistic regression as a 2 layer neural network. The 2nd part Deep Learning from first principles in Python, R and Octave-Part 2, dealt with the implementation of 3 layer Neural Networks with 1 hidden layer to perform classification tasks, where the 2 classes cannot be separated by a linear boundary. In this third part, I implement a multi-layer, Deep Learning (DL) network of arbitrary depth (any number of hidden layers) and arbitrary height (any number of activation units in each hidden layer). The implementations of these Deep Learning networks, in all the 3 parts, are based on vectorized versions in Python, R and Octave. The implementation in the 3rd part is for a L-layer Deep Netwwork, but without any regularization, early stopping, momentum or learning rate adaptation techniques. However even the barebones multi-layer DL, is a handful and has enough hyperparameters to fine-tune and adjust.

While testing with different hyper-parameters namely i) the number of hidden layers, ii) the number of activation units in each layer, iii) the activation function and iv) the number iterations, I found the L-layer Deep Learning Network to be very sensitive to these hyper-parameters. It is not easy to tune the parameters. Adding more hidden layers, or more units per layer, does not help and mostly results in gradient descent getting stuck in some local minima. It does take a fair amount of trial and error and very close observation on how the DL network performs for logical changes. We then can zero in on the most the optimal solution. Feel free to download/fork my code from Github DeepLearning-Part 3 and play around with the hyper-parameters for your own problems.

The number of elements between the first and the last element are the number of hidden layers and the magnitude of each is the number of activation units in each hidden layer, which is specified while actually executing the Deep Learning network using the function L_Layer_DeepModel(), in all the implementations Python, R and Octave

The code below uses the Tanh activation in the hidden layers for Octave# Read the datadata=csvread("data.csv");X=data(:,1:2);Y=data(:,3);# Set layer dimensionslayersDimensions = [2 9 7 1] #tanh=-0.5(ok), #relu=0.1 best!# Execute Deep Network[weights biases costs]=L_Layer_DeepModel(X', Y', layersDimensions,hiddenActivationFunc='tanh',learningRate = 0.1,numIterations = 10000);plotCostVsIterations(10000,costs);plotDecisionBoundary(data,weights, biases,hiddenActivationFunc="tanh")

I will be continuing this series with more hyper-parameters to handle vanishing and exploding gradients, early stopping and regularization in the weeks to come. I also intend to add some mor