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//
//  HudlHlsAvPlayerCache.m
//  Hudl
//
//  Created by Brian Clymer on 3/6/15.
//  Copyright (c) 2015 Agile Sports Technologies, Inc. All rights reserved.
//

#import "HudlHlsAvPlayerCache.h"

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# http://artsy.github.io/blog/2016/03/02/Lazily-Automation/

function git_branch_info
  git branch ^/dev/null | grep \* | sed 's/* //'
end

function branch
  git checkout master;
  git pull upstream master;
  git checkout -b $argv[1]

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kernel vec4 coreImageKernel(__color startColor, __color endColor)
{
	vec2 point = destCoord();

	float angle = atan(point.y, point.x) + radians(180.0);
	//Start from the upper middle, not the left middle
	angle += radians(90.0);
	angle = mod(angle, radians(360.0));

	float fraction = angle / radians(360.0);

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tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

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Ubuntu 14 on AWS GPU instance

Verify You Have a CUDA-Capable GPU

lspci | grep -i nvidia

Souce

• https://no2147483647.wordpress.com/2016/01/16/setup-amazon-aws-gpu-instance-with-mxnet/
• http://tleyden.github.io/blog/2015/11/22/cuda-7-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

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Ubuntu 14 on AWS GPU instance

Verify You Have a CUDA-Capable GPU

lspci | grep -i nvidia

Souce

• https://no2147483647.wordpress.com/2016/01/16/setup-amazon-aws-gpu-instance-with-mxnet/
• http://tleyden.github.io/blog/2015/11/22/cuda-7-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

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import asyncio
from datetime import datetime, timezone
import os

def utc_now():
    # utcnow returns a naive datetime, so we have to set the timezone manually <sigh>
    return datetime.utcnow().replace(tzinfo=timezone.utc)
  
class Terminator:
  pass

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FFmpeg and libav's playbook: Advanced encoding options with hardware-based acceleration, NVIDIA's NVENC and Intel's VAAPI-based encoder.

Hello guys,

Continuing from this guide to building ffmpeg and libav with NVENC and VAAPI enabled, this snippet will cover advanced options that you can use with ffmpeg and libav on both NVENC and VAAPI hardware-based encoders.

For ffmpeg:

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# Example for my blog post at:
# http://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
import functools
import sets
import tensorflow as tf


def lazy_property(function):
    attribute = '_' + function.__name__

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from keras.layers import Input, Dense
from keras.models import Model, Sequential
from keras.datasets import mnist
from keras.layers.normalization import BatchNormalization as BN

autoencoder1 = Sequential([
    Dense(128, activation='relu',input_shape=(784,)),
    BN(),
    Dense(784, activation='relu'),
])