supriya-gdptl

# install meshio: https://pypi.org/project/meshio/
pip install meshio

# run following in terminal to convert all .ply files from binary to ascii format INPLACE
find . -maxdepth 1 -type f -name "*.ply" -exec meshio-ascii "{}" \;
# . = current directory
# -maxdepth 1  = find files only in current directory and ignore subdirectories
# -type f = type of file to find 'regular file'
# -name "*.ply" = base of filename
# -exec [command] "{}" \;  = execute command on files found. The "{}" is placeholder for filename. 

supriya-gdptl

rsync -azh -P --rsh='ssh -p<dest_portnum>' <file_to_copy> <user>@host:<destination>

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# install Google Chrome on Ubuntu 14.04 64-bit on terminal
wget https://dl.google.com/linux/direct/google-chrome-stable_current_amd64.deb
sudo dpkg -i google-chrome-stable_current_amd64.deb

# source: https://itsfoss.com/install-chrome-ubuntu/

supriya-gdptl

screen -dmS test_screen_name

screen -S test_screen_name -X logfile name_of_logfile.txt

screen -S test_screen_name -X log

screen -r test_screen_name

[source: https://superuser.com/questions/766903/how-to-set-the-gnu-screen-log-file-name-non-interactively]

supriya-gdptl

Include package:

\documentclass{article}
\usepackage{natbib}
  
Options for the \cite command and their outputs:

\citet{key}	Jones et al. (1990)
\citet*{key}	Jones, Baker, and Smith (1990)
\citep{key}	(Jones et al. 1990)

supriya-gdptl

sudo apt-get update
sudo apt-get install firefox

supriya-gdptl

ps u pid_number  ->To see the owner of process 'pid_number'

screen -S [any_name] ->put code for running

Ctrl A D -> to detach the screen

screen -r [same_above_name] -> to resume screen

go to screen and type exit -> to terminate the screen
OR

supriya-gdptl

To install CUDA9.0 and cuDNN 7.0, follwo instruction given on following website:
https://medium.com/@zhanwenchen/install-cuda-and-cudnn-for-tensorflow-gpu-on-ubuntu-79306e4ac04e
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To install anaconda on new machine:
(To install perticular version of anaconda refer https://repo.continuum.io/archive/)

wget https://repo.continuum.io/archive/Anaconda3-5.1.0-Linux-x86_64.sh
bash Anaconda3-5.1.0-Linux-x86_64.sh

supriya-gdptl

- ![#f03c15](https://placehold.it/15/f03c15/000000?text=+) `#f03c15`
- ![#c5f015](https://placehold.it/15/c5f015/000000?text=+) `#c5f015`
- ![#1589F0](https://placehold.it/15/1589F0/000000?text=+) `#1589F0`

1st generates red block
2st generates green block
3st generates blue block

supriya-gdptl

1. Inductive Bias : It is a set of assumptions learner uses to predict results for unseen inputs. Every machine learning algorithm that is trained for generalization purpose has inductive bias. For example, linear regression model assumes that output Y is linearly dependent on input X. This introduces bias in the training called inductive bias. Similarly for SVM, inductive bias is: the classes are separated by large margin. Please refer to website for knowing inductive biases of other machine learning algorithms.

2. Self Organizing Maps :

• Self organizing maps(SOM) are special type of neural network used for unsupervised feature learning. SOM falls into the category of non-linear dimensionality reduction methods.

• Unlike normal neural network that apply error-correcting learning algorithm(e.g. backpropagation with gradient descent), SOM apply competitive learning algorithms.

• It maps mul