Sriharsh Bhyravajjula darthbhyrava

Linux

Libraries

Installed in darthbhyrava:

Input Device Sharing

barrier

REST Clients

ASUS TUF Keyboard Backlight Not Working on Ubuntu

I'm facing this exact issues on my ASUS TUF FX505.

The keyboard function keys don't work: FN + <Up>, FN + <Down>
/sys/class/leds/ (how linux kernel handles leds) does not have a corresponding device:

~[/sys/class/leds]$ tree .
.
├── asus-wireless::airplane -> ../../devices/LNXSYSTM:00/LNXSYBUS:00/ATK4002:00/leds/asus-wireless::airplane

Check if your system recognizes a graphics card: $ lspci | grep VGA ~
For NVIDIA cards, check the name of the card using the device ID: ~
Check the recommended NVIDIA Driver: $ ubuntu-drivers devices ~
Install the necessary drivers: $ sudo apt install nvidia-driver-xxx ~
Sometimes, default nouveau drivers can cause issues. Check if that's the case by looking at default kernel drivers: $ lspci -v
Make sure you disable the nouveau drivers and update the initramfs ~
If you have issues with UUID in dual boot, check this: [~](https://askubuntu.com/questions/1116778/how-to-set-the-resume-va

Input Device Sharing

barrier

REST Clients

insomnia

IDEs

sublime-text 3
atom

Building TensorFlow from source (TF 2.1.0, Ubuntu 19.10)

Why build from source?

The official instructions on installing TensorFlow are here: https://www.tensorflow.org/install. If you want to install TensorFlow just using pip, you are running a supported Ubuntu LTS distribution, and you're happy to install the respective tested CUDA versions (which often are outdated), by all means go ahead. A good alternative may be to run a Docker image.

I am usually unhappy with installing what in effect are pre-built binaries. These binaries are often not compatible with the Ubuntu version I am running, the CUDA version that I have installed, and so on. Furthermore, they may be slower than binaries optimized for the target architecture, since certain instructions are not being used (e.g. AVX2, FMA).

So installing TensorFlow from source becomes a necessity. The official instructions on building TensorFlow from source are here: h

Requirements

Real-time Spell Checker
Can be used in a chatbot
Preferably using ML/DL

ML/DL Based Solutions

(Supervised) Spelling transformation vectors, which capture the patterns in differences of GloVe embeddings of correct and incorrect spellings. link If we use sub-word leel embeddings using fasttext, say, things get much better as shown in this article by Haptik.ai, a large scale Indian chatbot maker.
(Semi-supervised) A pipeline which preprocesses correct spellings to incorrect counterparts and trains a seq2seq model on the pairs . link. There's also another approach with code
(Unsupervised) My friend and college senior

February 2018

Using the lambda in python. lambda x: clean_text(x)
re.MULTILINE
hashtag segmentation/splitting?
sklearn.feature_extraction, sklearn.decomposition,
Pandas df series?
[ ]

Biggies

Learn Pandas

IASNLP-2015 Project list

LTRC, IIIT-Hyderabad

Treebanking

#####1. Shallow Parsers for different languages#####

Description - POS Tagging and Chunking for Gujarati,Odia, Hindi, Bengali, Marathi, Telugu (individual project for each language)
We will implement many supervised algorithms including CRF, HMM, MaxEnt, SVM, some semi-supervised classification methods, finally an unsupervised one. Will try to implement Morph Analyzer if time permits. students need to annotate data, understand the challenges, compare results given by multiple Mentor: Pruthwik M
algorithms- students for each language - 1 / 2

	# Import the two libraries needed for our task: json and requests
	import json
	import requests
	# If requests isn't installed , open up terminal/command line and run `$ pip install requests`
	# pip comes pre-installed from Python 2.7 onwards

	# We're storing files in the current directory, change as necessary
	file = './source-'

	# We have to pass some parameters along with our GET request

	"""
	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)