pranavraja

package main

import (
	"context"
	"encoding/json"
	"errors"
	"fmt"
	"io"
	"io/ioutil"
	"log"

surya501

# Pytorch implementation of LSHHash from https://github.com/kayzhu/LSHash
# Homework: Try to implement LSHHash in pytorch to speed up meanshift.
# Motivation: i.e. why calculate all distances when you need only a few.

import numpy as np
import importlib
import torch_utils
importlib.reload(torch_utils)
from torch_utils import *

idanori

stuart-warren

$ brew install fswatch
$ alias pywatch='fswatch -r -0 --monitor=poll_monitor $(find . -name *.py) | xargs --null -n1 -I{}'
$ alias gowatch='fswatch -r -0 --monitor=poll_monitor $(find . -name *.go) | xargs --null -n1 -I{}'
$ pywatch python setup.py test
$ gowatch go test -cover

bzamecnik

"""
When traing ML models on text we usually need to represent words/character in one-hot encoding.
This can be done in preprocessing, however it may make the dataset file bigger. Also when we'd
like to use an Embedding layer, it accepts the original integer indexes instead of one-hot codes.

Can be move the one-hot encoding from pre-preprocessing directly into the model?

If so we could choose from two options: use one-hot inputs or perform embedding.

A way how to do this was suggested in Keras issue [#3680](https://github.com/fchollet/keras/issues/3680).

krtx

#!/bin/bash

# tmux requires unrecognized OSC sequences to be wrapped with DCS tmux;
# <sequence> ST, and for all ESCs in <sequence> to be replaced with ESC ESC. It
# only accepts ESC backslash for ST.
function print_osc() {
    if [[ -n $TERM ]] ; then
        printf "\033Ptmux;\033\033]"
    else
        printf "\033]"

tpruvot

# To allow nvidia powermizer settings, we need to define dummy monitors for each card
# Edit the PCI BusId for each cards (lspci | grep VGA)
# Also, you need to dump an EDID file from your monitor

# Here is my dell.edid (2560x1440)
# it's stored in text, hexdump -C format, not binary:
#  00 ff ff ff ff ff ff 00 10 ac 7e 40 4c 54 41 41
#  15 17 01 03 80 3c 22 78 ea 4b b5 a7 56 4b a3 25
#  0a 50 54 a5 4b 00 81 00 b3 00 d1 00 71 4f a9 40
#  81 80 d1 c0 01 01 56 5e 00 a0 a0 a0 29 50 30 20

dswah

from keras import backend as K
from keras import activations, initializations, regularizers, constraints
from keras.engine import Layer, InputSpec
from keras.utils.np_utils import conv_output_length
from keras.layers import Convolution1D, Convolution2D
import tensorflow as tf

class Convolution1D_tied(Layer):
    '''Convolution operator for filtering neighborhoods of one-dimensional inputs.
    When using this layer as the first layer in a model,

Brainiarc7

Using VAAPI's hardware accelerated video encoding on Linux with Intel's hardware on FFmpeg and libav

Hello, brethren :-)

As it turns out, the current version of FFmpeg (version 3.1 released earlier today) and libav (master branch) supports full H.264 and HEVC encode in VAAPI on supported hardware that works reliably well to be termed "production-ready".

plurch

exports.knex = require('knex')({
  client: 'pg',
  connection: {
    host     : '127.0.0.1',
    user     : 'your_database_user',
    password : 'your_database_password',
    database : 'myapp_test'
  }
});