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Vishal Jain jainxy

  • Bengaluru, India
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View colab_commands.txt
# 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')
@jainxy
jainxy / colab_md.txt
Last active Apr 8, 2019
Markdown - Quick Commands
View colab_md.txt
-> 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~~
@jainxy
jainxy / widget_commands.txt
Created Apr 10, 2019
Colab Layout Widget
View widget_commands.txt
# 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):
View io_colab.txt
# 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.
@jainxy
jainxy / scikit_nb.txt
Created Apr 11, 2019
Naive Bayes - sklearn
View scikit_nb.txt
# 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
View data_comparison.ipynb
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@jainxy
jainxy / min-char-rnn.py
Created Feb 14, 2020 — forked from karpathy/min-char-rnn.py
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
View min-char-rnn.py
"""
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)
@jainxy
jainxy / bash_shell_scripting.md
Last active Feb 21, 2020
Bash shell scripting
View bash_shell_scripting.md

References

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.
View crontab_tricks.txt
=== Use conda environment to run a python script ===
SHELL=/bin/bash
CONDA_PREFIX=/home/code/packages/miniconda3
CONDA_INIT="/home/code/packages/miniconda3/etc/conda/activate.d/proj4-activate.sh"
PYTHON=/home/code/packages/miniconda3/bin/python
12 13 * * * . $CONDA_INIT ; $PYTHON <python file>
=== Use date utility for naming a log file ===
DATEVAR="date +%Y%m%d_%H%M"
17 * * * * echo "logs_$($DATEVAR).txt"
@jainxy
jainxy / code_snips_py.txt
Created Mar 19, 2020
Code snippets - Python
View code_snips_py.txt
=======================
FLASK module ->
from flask import Flask, g, request, redirect, url_for
=======================