gam-phon

version: "3"
# Thanks to https://visibilityspots.org/dockerized-cloudflared-pi-hole.html
# echo "alias dns-up='cd /path/to/folder; docker-compose up -d'" >> ~/.bash_profile
services:
  dns-over-https:
    container_name: dns-over-https
    image: fardog/secureoperator:latest # google dns
    # image: visibilityspots/cloudflared:amd64 # cloudflare dns
    restart: unless-stopped
    networks:

ishankhatri90

# From https://www.raspberrypi.org/forums/viewtopic.php?f=29&t=24679&start=50

hdmi_ignore_cec_init=1
hdmi_drive=2
disable_overscan=1

hdmi_ignore_edid=0xa5000080
hdmi_group=2
hdmi_mode=87
hdmi_timings=2560 1 64 64 96 1080 1 3 10 31 0 0 1 60 0 185580000 8

ljaraque

Install tensorflow-gpu1.8 in ubuntu18.04 with CUDA9.2, cuDNN7.2.1 and NVIDIA Driver 396

ljaraque@yahoo.com

Overview

This is a summary of the process I lived in order to enable my system with CUDA9.2, cuDNN7.2.1, Tensorflow1.8 and NVIDIA GEFORCE GTX860M GPU. You can just skip the steps marked with FAILED. I decided to keep them there in order to be useful for others who tried those paths too.

FAILED (Next section is successfull) Install NVIDIA driver (FAILED, THIS WILL INSTALL DRIVER 390 which is not compatible with CUDA9.2):

ubuntu-drivers devices

xoseperez

Raspberry Pi 3 IoT Home Server

Presentation

http://tinkerman.cat/rpi3_iot_server.pdf (Catalan)

Get the latest image and flash the SD card

• download the latest image

LucasBonafe

NetLimiter 3
Registration name:  Peter Raheli
Registration code:  C99A2-QSSUD-2CSBG-TSRPN-A2BEB

NetLimiter 4
Registration Name:  Vladimir Putin #2
Registration Code:  XLEVD-PNASB-6A3BD-Z72GJ-SPAH7

https://www.netlimiter.com/download
# Netlimiter Full Netlimiter Activated Netlimiter cracked Netlimiter Full Version Netlimiter Serial Netlimiter keygen Netlimiter crack Netlimiter 4 serial Netlimiter 4 Crack Netlimiter 4 register Netlimiter 4 patch Netlimiter full Full version Netlimiter 4 Activated Netlimiter 4 Cracked Netlimiter Pro

umidjons

Download Audio from YouTube

-i - ignore errors

-c - continue

-t - use video title as file name

--extract-audio - extract audio track

kmhofmann

Building TensorFlow from source (TF 2.3.0, Ubuntu 20.04)

Why build from source?

The official instructions on installing TensorFlow are here: https://www.tensorflow.org/install. If you want to install TensorFlow just using pip, you are running a supported Ubuntu LTS distribution, and you're happy to install the respective tested CUDA versions (which often are outdated), by all means go ahead. A good alternative may be to run a Docker image.

I am usually unhappy with installing what in effect are pre-built binaries. These binaries are often not compatible with the Ubuntu version I am running, the CUDA version that I have installed, and so on. Furthermore, they may be slower than binaries optimized for the target architecture, since certain instructions are not being used (e.g. AVX2, FMA).

So installing TensorFlow from source becomes a necessity. The official instructions on building TensorFlow from source are here: ht

dantheman213

youtube-dl cheat sheet

Docs and Binary for youtube-dl are located here:

https://github.com/rg3/youtube-dl/

Install dependencies

apt-get install libav-tools ffmpeg # Linux (either one of either should do) brew install ffmpeg # OSX choco install ffmpeg # Windows

bzamecnik

"""
When classifying upon a sequence usually we stack some LSTM returning sequences,
then one LSTM returning a point, then Dense with softmax activation.

Is it possible instead to give the last non-sequential LSTM a softmax activation?

The answer is yes.

In this example we have 3 sequential layers and one layer producing the final result.

dmyersturnbull

import pandas as pd
import itertools
import time
import multiprocessing
from typing import Callable, Tuple, Union

def groupby_parallel(
  groupby_df: pd.core.groupby.DataFrameGroupBy,
  func: Callable[[Tuple[str, pd.DataFrame]], Union[pd.DataFrame, pd.Series]],
  num_cpus: int = multiprocessing.cpu_count() - 1,