cibomahto

//! @brief Pattern file recorder/playback
//!
//! The purpose is to allow capure and playback of streamed pattern data. The
//! recorder is intended to capture raw data and sync packets directly from the
//! listener (ie, before mapping or color/brightness manipulation is applied).
//! During playback, the raw packets are sent to the mapper for processing.
//! This allows the mapping and output settings to be adjusted after recording.
//!
//! Packets are recorded with a time resolution of 1 ms.
//!

memo

'use strict';

const fetch = require('node-fetch');
const msautils = require('./utils');

let apikey;


function setApiKey(_apikey) {
    apikey = _apikey;

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

Mahedi-61

#!/bin/bash

### steps ####
# Verify the system has a cuda-capable gpu
# Download and install the nvidia cuda toolkit and cudnn
# Setup environmental variables
# Verify the installation
###

### to verify your gpu is cuda enable check

carlthome

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:

victor-shepardson

from contextlib import contextmanager
import numpy as np
import torch
from torch import Tensor, ByteTensor
import torch.nn.functional as F
from torch.autograd import Variable
import pycuda.driver
from pycuda.gl import graphics_map_flags
from glumpy import app, gloo, gl

poolio

MadLittleMods

Realtek 8153

Download

Current version: 1.0.19 1.0.15 (as of 2018-12-10)

https://www.realtek.com/en/component/zoo/category/network-interface-controllers-10-100-1000m-gigabit-ethernet-usb-3-0-software

http://www.realtek.com.tw/downloads/downloadsView.aspx?Langid=1&PNid=13&PFid=56&Level=5&Conn=4&DownTypeID=3&GetDown=false

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.