f0k

MIT License

Copyright (c) 2017 Jan Schlüter

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

comex

#include <sys/socket.h>
#include <sys/ioctl.h>
#include <sys/kern_event.h>
#include <stdio.h>
#include <stdint.h>
#include <stdbool.h>
#include <stdlib.h>
#include <assert.h>
#include <string.h>
#include <net/ethernet.h>

shagunsodhani

The Batch Normalization paper describes a method to address the various issues related to training of Deep Neural Networks. It makes normalization a part of the architecture itself and reports significant improvements in terms of the number of iterations required to train the network.

Issues With Training Deep Neural Networks

Internal Covariate shift

Covariate shift refers to the change in the input distribution to a learning system. In the case of deep networks, the input to each layer is affected by parameters in all the input layers. So even small changes to the network get amplified down the network. This leads to change in the input distribution to internal layers of the deep network and is known as internal covariate shift.

It is well established that networks converge faster if the inputs have been whitened (ie zero mean, unit variances) and are uncorrelated and internal covariate shift leads to just the opposite.

poolio

knandersen

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
USAGE: 
morphagene_ableton.py -w <inputwavfile> -l <inputlabels> -o <outputfile>'

Instructions in Ableton:
    Insert locators as splice markers in your project (Create > Add Locator)
    Export Audio/Video with
        Sample Rate: 48000 Hz

bmcfee

jamiew

easterEgg.BadWorder.list={
"4r5e":1,
"5h1t":1,
"5hit":1,
a55:1,
anal:1,
anus:1,
ar5e:1,
arrse:1,
arse:1,

baraldilorenzo

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

carlthome

yanofsky

This is free and unencumbered software released into the public domain.

Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any
means.

In jurisdictions that recognize copyright laws, the author or authors
of this software dedicate any and all copyright interest in the
software to the public domain. We make this dedication for the benefit