Peter Alcock peteralcock

## spacy_intro.ipynb

      
              1 file
            
          
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                aparrish
                / spacy_intro.ipynb
            
            
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              March 14, 2025 21:43
            
              
                NLP Concepts with spaCy. Code examples released under CC0 https://creativecommons.org/choose/zero/, other text released under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/
              
          
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## base.Dockerfile
# Published on Docker Hub with above user alexellisio.

# If you want to rebuild your own copy, follow below instructions
# Build this on each type of machine so you have the correct CPU extensions.
FROM alexellisio/boostbase

RUN git clone -b Linux https://github.com/nicehash/nheqminer.git
RUN cd nheqminer/cpu_xenoncat/Linux/asm/ && sh assemble.sh && cd ../../../Linux_cmake/nheqminer_cpu && cmake . && make

ENTRYPOINT ["./nheqminer/Linux_cmake/nheqminer_cpu/nheqminer_cpu"]

## install-docker-master.sh
#!/bin/sh
# option 2: paste this into user-data to automate install via boot script
# NOTE: update --label=owner=YOURNAME below if you want to easily identify yours

# renames the host to have a suffix of alexellisio
export original=$(cat /etc/hostname)
sudo hostname $original-master-alexellisio
echo $original-master-alexellisio | sudo tee /etc/hostname

apt-key adv --keyserver hkp://p80.pool.sks-keyservers.net:80 --recv-keys 58118E89F3A912897C070ADBF76221572C52609D

## classifier_from_little_data_script_3.py
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

## _README.md

      
              2 files
            
          
              24 forks
            
          
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              167 stars
            
          
                oleq
                / _README.md
            
            
              Last active
              November 16, 2025 12:17
            
              
                A2DP audio streaming using Raspberry PI (Raspbian Jessie)
              
          
    What is this all about?

This tutorial will turn your Raspberry PI into a simple Bluetooth audio receiver, which plays music through connected speakers. It's like a regular car audio system, but it can be used anywhere and it's a good value.
   Audio source (i.e. smartphone) 
                |
                v
 (((  Wireless Bluetooth Channel  )))
 |


## dashboard
#!/bin/bash
# chkconfig: 2345 55 25
# Dashing service
# Add this file to /etc/init.d/
# Make executable
# Configure chkconfig
# Dashboard will start at boot. Check out the boot log for trouble shooting "/var/log/boot.log"
######################################################
#YOU WILL NEED TO CONFIGURE THIS FOR YOUR ENVIRONMENT#
######################################################

## gist:2788529
$ brew uninstall postgresql
$ gem uninstall pg # ALL OF THEM
$ rm -fr /usr/local/var/postgres
$ launchctl unload -w ~/Library/LaunchAgents/homebrew.mxcl.postgresql.plist
$ pg_ctl -D /usr/local/var/postgres stop -s -m fast # WE SHOULD HAVE ALL postgres SERVERS & PROCESSES STOPPED BY NOW
$ brew install postgres
$ env ARCHFLAGS="-arch x86_64" gem install pg
$ echo "DONE." >> /dev/null
	# Published on Docker Hub with above user alexellisio.

	# If you want to rebuild your own copy, follow below instructions
	# Build this on each type of machine so you have the correct CPU extensions.
	FROM alexellisio/boostbase

	RUN git clone -b Linux https://github.com/nicehash/nheqminer.git
	RUN cd nheqminer/cpu_xenoncat/Linux/asm/ && sh assemble.sh && cd ../../../Linux_cmake/nheqminer_cpu && cmake . && make

	ENTRYPOINT ["./nheqminer/Linux_cmake/nheqminer_cpu/nheqminer_cpu"]
	#!/bin/sh
	# option 2: paste this into user-data to automate install via boot script
	# NOTE: update --label=owner=YOURNAME below if you want to easily identify yours

	# renames the host to have a suffix of alexellisio
	export original=$(cat /etc/hostname)
	sudo hostname $original-master-alexellisio
	echo $original-master-alexellisio \| sudo tee /etc/hostname

	apt-key adv --keyserver hkp://p80.pool.sks-keyservers.net:80 --recv-keys 58118E89F3A912897C070ADBF76221572C52609D
	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats
	#!/bin/bash
	# chkconfig: 2345 55 25
	# Dashing service
	# Add this file to /etc/init.d/
	# Make executable
	# Configure chkconfig
	# Dashboard will start at boot. Check out the boot log for trouble shooting "/var/log/boot.log"
	######################################################
	#YOU WILL NEED TO CONFIGURE THIS FOR YOUR ENVIRONMENT#
	######################################################
	$ brew uninstall postgresql
	$ gem uninstall pg # ALL OF THEM
	$ rm -fr /usr/local/var/postgres
	$ launchctl unload -w ~/Library/LaunchAgents/homebrew.mxcl.postgresql.plist
	$ pg_ctl -D /usr/local/var/postgres stop -s -m fast # WE SHOULD HAVE ALL postgres SERVERS & PROCESSES STOPPED BY NOW
	$ brew install postgres
	$ env ARCHFLAGS="-arch x86_64" gem install pg
	$ echo "DONE." >> /dev/null