hiraksarkar

hiraksarkar

#!/bin/bash
## This gist contains instructions about cuda v11.2 and cudnn8.1 installation in Ubuntu 20.04 for Pytorch 1.8 & Tensorflow 2.7.0

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

hiraksarkar

library(ggplot2)
library(dplyr)
library(ggbump)

tmp.subset = lr_df_merged %>% filter(ligand %in% c('WNT9A','ARTN', 'ANGPTL2') )
tmp.subset = tmp.subset |> arrange(ligand,desc(copula_coeff))

to_nodes = distinct(tmp.subset, receptor) |> mutate(to_y = row_number())
num_senders = (distinct(tmp.subset, ligand) %>% dim)[[1]]
from_nodes = distinct(tmp.subset, ligand) |> mutate(from_y = round(dim(to_nodes)[[1]])/(num_senders+1) + row_number()-1+0.5)

hiraksarkar

Basic

Installing R packages is painful, but conda environment solved a lot of problems. Basically, if you install your own R in conda, and the later R command install.packages() will automatically install the packages in the environment; in addition, conda has many system libraries too for getting away from requiring sudo permissions.

After creating an empty conda environment, you can install a specific version (say 4.2) of R by conda install -c conda-forge r-essentials=4.2. If you are not sure whether that version exists in conda, you can do conda search r-essentials. Using your own R in the conda environment, you can do the normal R installation commands.

Some R packages search system libraries by pkg-config. After you install the required libraries through conda, you can check whether your PKG_CONFIG_PATH includes /envs//lib/pkgconfig, and set the path properly.

hiraksarkar

Step 0

• MV Gaussian link
• Math4ML video playlist
• Deep Learning foundation and concept book Chapter 1-3
• GLM theory link

Regression and Statistics

The main proofs are to be read from the following order

hiraksarkar

The main proofs are to be read from the following order -- Proofs are given here https://www.statlect.com/fundamentals-of-statistics/ It should be accompanied by the econometrics lecture given here and here

These can be accompanied by the following books

• Foundations of Statistics for Data Scientists R and Python by Agresti and Kateri
• Introductory Econometrics: A Modern Approach by Woodridge

For algorithmic treatment that talks about efficient mechanisms to optimize consult

hiraksarkar

Given the two recent books are out the best is to use them iteratively. I am not gonna add the book by Murphy as I find it not to be self sufficient but rather regard it as a dictionary. Main books

• Understanding deep learning by Prince book and notebook
• Deep Learning foundation and concept book

Murphy's book for reference

• Probabilistic Machine Learning: Advanced Topics book

hiraksarkar

#!/bin/bash
#SBATCH --mincpus 32
#SBATCH --mem 100G
#SBATCH --time 6-23:59:00
#SBATCH --job-name jupyterlab
#SBATCH --gres=gpu:1
#SBATCH --mail-type=begin        # send email when job begins
#SBATCH --mail-type=end          # send email when job ends
#SBATCH --mail-user=hs0424@princeton.edu
#SBATCH --output jupyter_logs/jupyter-notebook-%J.log

hiraksarkar

#!/bin/bash
#SBATCH --mincpus 16
#SBATCH --mem 64G
#SBATCH --time 5:00:00
#SBATCH --job-name mendel
#SBATCH --mail-type=begin        # send email when job begins
#SBATCH --mail-type=end          # send email when job ends
#SBATCH --mail-user=hs0424@princeton.edu
#SBATCH --output jupyter_logs/jupyter-notebook-%J.log
# get tunneling info

hiraksarkar

mamba create -n r43
mamba activate r43
mamba install r-essentials=4.3
mamba install r-rjags
export PKG_CONFIG_PATH=/home/user/miniconda3/envs/r43/lib/pkgconfig/:$PKG_CONFIG_PATH

# start R
R

# Install R package