Arun Das arundasan91

Letting Ubuntu bash on Windows 10 run 'ssh -X' to get a GUI environment on a remote server

First

Install all the following. On Window, install Xming. On Ubuntu bash, use sudo apt install to install ssh xauth xorg.

sudo apt install ssh xauth xorg

How to install virtualenv:

Install pip first

sudo apt-get install python3-pip

Then install virtualenv using pip3

sudo pip3 install virtualenv

Oh my zsh.

Install with curl

sh -c "$(curl -fsSL https://raw.githubusercontent.com/robbyrussell/oh-my-zsh/master/tools/install.sh)"

Enabling Plugins (zsh-autosuggestions & zsh-syntax-highlighting)

Download zsh-autosuggestions by

Freshly brewed !

With the availability of huge amount of data for research and powerfull machines to run your code on, Machine Learning and Neural Networks is gaining their foot again and impacting us more than ever in our everyday lives. With huge players like Google opensourcing part of their Machine Learning systems like the TensorFlow software library for numerical computation, there are many options for someone interested in starting off with Machine Learning/Neural Nets to choose from. Caffe, a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and its contributors, comes to the play with a fresh cup of coffee.

Installation Instructions (Ubuntu 14 Trusty)

The following section is divided in to two parts. Caffe's documentation suggest

Git-workflow vs feature branching

When working with Git, there are two prevailing workflows are Git workflow and feature branches. IMHO, being more of a subscriber to continuous integration, I feel that the feature branch workflow is better suited, and the focus of this article.

If you are new to Git and Git-workflows, I suggest reading the atlassian.com Git Workflow article in addition to this as there is more detail there than presented here.

I admit, using Bash in the command line with the standard configuration leaves a bit to be desired when it comes to awareness of state. A tool that I suggest using follows these instructions on setting up GIT Bash autocompletion. This tool will assist you to better visualize the state of a branc

	"""
	Play with saving .

	Closest:
	https://github.com/tensorflow/tensorflow/issues/616#issuecomment-205620223
	"""
	import numpy as np

	import tensorflow as tf
	from tensorflow.python.platform import gfile

	'''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