Make sure there is at least one file in it (even just the README.md)
ssh-keygen -t rsa -C "[email protected]"
deb http://archive.ubuntu.com/ubuntu/ focal main restricted universe multiverse | |
deb-src http://archive.ubuntu.com/ubuntu/ focal main restricted universe multiverse | |
deb http://archive.ubuntu.com/ubuntu/ focal-updates main restricted universe multiverse | |
deb-src http://archive.ubuntu.com/ubuntu/ focal-updates main restricted universe multiverse | |
deb http://archive.ubuntu.com/ubuntu/ focal-security main restricted universe multiverse | |
deb-src http://archive.ubuntu.com/ubuntu/ focal-security main restricted universe multiverse | |
deb http://archive.ubuntu.com/ubuntu/ focal-backports main restricted universe multiverse |
Make sure there is at least one file in it (even just the README.md)
ssh-keygen -t rsa -C "[email protected]"
numbers = [10, 45, 20, 67, 29, 12, 15, 99, 40, 23] | |
# Repalce the "None" values with your solutions | |
last_number = None | |
before_last_number = None | |
first_number = None | |
seventh_number = None | |
first_five_numbers = None | |
last_three_numbers = None | |
# Get the numbers from the third number (inclusive) to the sixth number (inclusive) |
#!/bin/bash | |
# This script will install AMDGPU-PRO OpenCL and Vulkan support. | |
# | |
# For Ubuntu and it's flavor, just install the package using this command | |
# in extracted driver directory instread. | |
# | |
# ./amdgpu-pro-install --opencl=legacy,pal --headless --no-dkms | |
# | |
# For Arch Linux or Manjaro, use the opencl-amd on AUR instread. |
This is a companion piece to my instructions on building TensorFlow from source. In particular, the aim is to install the following pieces of software
on an Ubuntu Linux system, in particular Ubuntu 20.04.
# variation to https://github.com/ryankiros/skip-thoughts/blob/master/decoding/search.py | |
def keras_rnn_predict(samples, empty=empty, rnn_model=model, maxlen=maxlen): | |
"""for every sample, calculate probability for every possible label | |
you need to supply your RNN model and maxlen - the length of sequences it can handle | |
""" | |
data = sequence.pad_sequences(samples, maxlen=maxlen, value=empty) | |
return rnn_model.predict(data, verbose=0) | |
def beamsearch(predict=keras_rnn_predict, |
<?xml version="1.0" standalone='no'?> | |
<!DOCTYPE service-group SYSTEM "avahi-service.dtd"> | |
<service-group> | |
<name replace-wildcards="yes">%h</name> | |
<service> | |
<type>_adisk._tcp</type> | |
<txt-record>sys=waMa=0,adVF=0x100</txt-record> | |
<txt-record>dk0=adVN=Time Capsule,adVF=0x82</txt-record> | |
</service> | |
<service> |