p-i-

using Complex = std::complex<float>;

class FormantFilter {
	float a1, a2, yPrev, yPrevPrev;

public:
	float out;

	FormantFilter(float freq, float bandwidth, float sampleRate, float gainDB)
	{

p-i-

using Complex = std::complex<float>;

class FormantFilter {
	float a1, a2, yPrev, yPrevPrev;

public:
	float out;

	FormantFilter(float freq, float bandwidth, float sampleRate, float gainDB)
	{

p-i-

using Complex = std::complex<float>;

struct Formant {
private:
	float freq, bandwidth, gain_db, sampleRate;

	float a1, a2, b0;
	float yPrev, yPrevPrev;

public:

p-i-

"""Implements Assignment 3 for Geoffrey Hinton's Neural Networks Course offered through Coursera.

* (Batch)Trains a simple Feedforward Neural Network with Backpropogation, for recognizing USPS handwritten digits.
* Assignment looks into efficient optimization, and into effective regularization.
* Recognizes USPS handwritten digits.

Abstracts classifiers developed in the course into, a more pythonic Sklearn framework. And cleans up a lot of the
given code.

* NOTE Matrix sizes are given as <y> <x> i.e. <#rows> <#cols>

p-i-

"""Implements Assignment 3 for Geoffrey Hinton's Neural Networks Course offered through Coursera.

* (Batch)Trains a simple Feedforward Neural Network with Backpropogation, for recognizing USPS handwritten digits.
* Assignment looks into efficient optimization, and into effective regularization.
* Recognizes USPS handwritten digits.

Abstracts classifiers developed in the course into, a more pythonic Sklearn framework. And cleans up a lot of the
given code.

* NOTE Matrix sizes are given as <y> <x> i.e. <#rows> <#cols>

p-i-

"""Implements Assignment 3 for Geoffrey Hinton's Neural Networks Course offered through Coursera.

* (Batch)Trains a simple Feedforward Neural Network with Backpropogation, for recognizing USPS handwritten digits.
* Assignment looks into efficient optimization, and into effective regularization.
* Recognizes USPS handwritten digits.

Abstracts classifiers developed in the course into, a more pythonic Sklearn framework. And cleans up a lot of the
given code.

* NOTE Matrix sizes are given as <y> <x> i.e. <#rows> <#cols>

p-i-

% Version 1.000
%
% Code provided by Ruslan Salakhutdinov
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying that the original programs are available from our
% web page.
% The programs and documents are distributed without any warranty, express or

p-i-

function [pd, sample] = softMax( inputs, weights, biases )
    nOutputs = size( biases, 2 );

    batchSize = size( inputs, 1 );

    z = inputs * weights + repmat( biases, batchSize, 1 );
    expZ = exp( z );

    %pd = expZ ./ ( sum(expZ,2) * ones(1,nOutputs) );
    pd = expZ ./ repmat( sum(expZ,2), 1, nOutputs );

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    for epoch = 1 : maxepoch
        %%% Do Conjugate Gradient Optimization
        
        fprintf( 1, 'epoch %d, batch: ', epoch );
        randomBatch = randperm(600);
        for b = 1 : nBatches_train/100, % do 100 batches at a go
            fprintf(1, ' %d', b);
            
            data    = zeros(10000,neurons0);
            pd2_all = zeros(10000,neurons2);

p-i-

HowTo for installing bleeding-edge (a.k.a. Nightly/Master) Juno

A Guide for (other) idiots

This guide is for you if:

• you wish to understand how Juno works under the hood
• you like taking things apart, tinkering, and putting them back together
• you want to be on the bleeding edge build, why?

• if you report bugs the devs will like you more