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hiraksarkar / ML_gude.md
Last active December 12, 2023 17:29
ML guided tour
View ML_gude.md

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
hiraksarkar / deeplearning_guide.md
Created December 12, 2023 17:03
Deep Learning Guided Tour
View deeplearning_guide.md

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
hiraksarkar / regression_guided_tour.md
Last active December 12, 2023 17:03
Regression book tour with proofs and code
View regression_guided_tour.md

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
hiraksarkar / install_infercnv.sh
Created September 14, 2023 04:50
Install infercnv in conda
View install_infercnv.sh
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
@hiraksarkar
hiraksarkar / R_installation_instruction.md
Last active January 21, 2024 21:13
R_installation_instruction within conda
View R_installation_instruction.md

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.

View cycles_princeton_cs_slurm_jupyter.sh
#!/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
hiraksarkar / della_princeton_jupyter_slurm.sh
Created September 9, 2023 16:54
della slurm jupyter job submission
View della_princeton_jupyter_slurm.sh
#!/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
View cuda_11.2_installation_on_Ubuntu_20.04
#!/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
hiraksarkar / use_vscode_with_colab.md
Last active March 13, 2021 16:40
Quick intro to using colab virtual machine for quick GPU access
View use_vscode_with_colab.md

Note

I have changed only two directory names from the original post to be found here

Open account with ngrok.com

Get the autorization code after creating a free account with ngork. You will meet with a screen like following

Install stuff (in colab)

@hiraksarkar
hiraksarkar / some_cool_plots.py
Created December 11, 2020 02:00
some cool plots
View some_cool_plots.py
plt.figure(figsize=(8, 8))
df = np.log(variance_trio_df[['var_mean','merged_var']]+1)
df.loc[:,'color'] = 'b'
df.loc[(df.merged_var - df.var_mean) < -1, 'color'] = 'r'
colors = ["b", "r"]
customPalette = sns.set_palette(sns.color_palette(colors))
ax = sns.scatterplot(
x = 'var_mean',
y = 'merged_var',
data = df,