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# Execute Colab notebook on local Jupyter Runtime - https://research.google.com/colaboratory/local-runtimes.html

# Mount Google drive to collab notebooks to use files and folders into the notebook
# Ref : https://towardsdatascience.com/getting-started-with-google-colab-f2fff97f594c

from google.colab import drive
drive.mount('/content/gdrive')

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-> Markdown => Simple Markup language
-> Colab's MD => Rendered via marked.js and similar to those in Jupyter and Github's version
-> Difference: Supports Latex equations(MathJax) like Jupyter but not HTML tags
->             Syntax Highlighting in the code blocks
****************HEADINGS**************
# This is equivalent to an <h1> tag

##### This is equivalent to an <h5> tag

_A text in italics_ ; __A text in bold__ ; ~~strike-through text~~

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# Ref - https://colab.research.google.com/notebooks/widgets.ipynb
# Build and control layout dynamically through code

from google.colab import widgets

# ----GRIDS-----
grid = widgets.Grid(2, 2, header_row=True, header_column=True)  # Grid object

# Grid.output_to(indices) to direct output stream to that place in the grid.
with grid.output_to(1, 1):

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# Ref -> https://colab.research.google.com/notebooks/io.ipynb#scrollTo=BaCkyg5CV5jF
# ---------- UPLOAD FROM LOCAL FILE SYSTEM --------------------
# files.upload returns a dictionary of the files which were uploaded. The dictionary is keyed by the file name, the value is the data which was uploaded.

# files.download will invoke a browser download of the file to the user's local computer.

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# load the iris dataset 
from sklearn.datasets import load_iris 
iris = load_iris() 
  
# store the feature matrix (X) and response vector (y) 
X = iris.data 
y = iris.target 
  
# splitting X and y into training and testing sets 

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------------------------------
VSCode
https://realpython.com/python-development-visual-studio-code/
https://flaviocopes.com/vscode/
https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.vscode-remote-extensionpack - DONE
------------------------------
RPN notes

https://medium.com/@jonathan_hui/understanding-region-based-fully-convolutional-networks-r-fcn-for-object-detection-828316f07c99 - DONE
https://medium.com/@jonathan_hui/what-do-we-learn-from-region-based-object-detectors-faster-r-cnn-r-fcn-fpn-7e354377a7c9 - DONE

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References - 
1. https://www.howtoforge.com/tutorial/how-to-create-docker-images-with-dockerfile/
2. https://takacsmark.com/dockerfile-tutorial-by-example-dockerfile-best-practices-2018/
3. https://docs.docker.com/engine/reference/builder/
Notes - 
 - A script or receipe having collection of commands and instructions, which will be executed in sequence, to build a new docker image.
 - Below are some Dockerfile commands and their usage.

1. FROM
 - Base docker image to build new image on top. This is 1st command in the Dockerfile. E.g. dockerized ubuntu image.

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"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

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References

• Bash Scripting: Everything you need to know about Bash-shell programming

Notes

Introduction

• Bash (AKA Bourne Again Shell) is a type of interpreter that processes shell commands.
• A shell interpreter takes commands in plain text format and calls Operating System services to do something.
• Bash is the improved version of Sh (Bourne Shell).
• A shell scripting is writing a program for the shell to execute and a shell script is a file or program that shell will execute.
• Terminal is the interface to the shell interpreter.
• A shell script is a fully-fledged programming language in itself. It can define variables, functions and we can do conditional execution of shell commands as well.