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

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

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 / use_vscode_with_colab.md
Last active March 13, 2021 16:40
Quick intro to using colab virtual machine for quick GPU access

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)

@AFAgarap
AFAgarap / autoencoder-full.py
Last active March 27, 2022 14:56
TensorFlow 2.0 implementation for a vanilla autoencoder. Link to tutorial: https://medium.com/@abien.agarap/implementing-an-autoencoder-in-tensorflow-2-0-5e86126e9f7
"""TensorFlow 2.0 implementation of vanilla Autoencoder."""
import numpy as np
import tensorflow as tf
__author__ = "Abien Fred Agarap"
np.random.seed(1)
tf.random.set_seed(1)
batch_size = 128
epochs = 10
@Mahedi-61
Mahedi-61 / cuda_11.8_installation_on_Ubuntu_22.04
Last active May 4, 2024 14:18
Instructions for CUDA v11.8 and cuDNN 8.9.7 installation on Ubuntu 22.04 for PyTorch 2.1.2
#!/bin/bash
### steps ####
# Verify the system has a cuda-capable gpu
# Download and install the nvidia cuda toolkit and cudnn
# Setup environmental variables
# Verify the installation
###
### to verify your gpu is cuda enable check
@apfejes
apfejes / gist:95405620abb347e1abc87d5708dbc003
Created January 12, 2018 19:55
A script for NCBI SRA downloads.
import argparse
default_prefix = "ftp.sra.ebi.ac.uk/vol1/fastq/"
ascp_prefix = "ftp.sra.ebi.ac.uk:/vol1/fastq/"
default_protocol = "ftp://"
default_suffix = "*.fastq.gz"
default_ascp_line = "ascp -QT -l 300m -i ~/.ssh/asperaweb_id_dsa.openssh anonftp@ftp.ncbi.nlm.nih.gov:{} {}"
def prepare_url(srr_name, prefix):
https://www.youtube.com/watch?v=F0sRpgnFyMY
## Evil mode
/ Start search in current buffer
n find next
N find previous
SPC s c clear the searches
:s/text/newtext/gc Search and replace
SPC s h Symbol highlight + options for search, multiple edit,...
# vim: set ft=snakemake:
shell.prefix("""set -euo pipefail;
if [ -e /lscratch/$SLURM_JOBID ]; then
export TMPDIR=/lscratch/$SLURM_JOBID
else
export TMPDIR=/tmp
fi;
""")
workdir: '.'
@santisbon
santisbon / Update-branch.md
Last active March 21, 2024 15:50
Deploying from #Git branches adds flexibility. Bring your feature branch up to date with master and deploy it to make sure everything works. If everything looks good the branch can be merged. Otherwise, you can deploy your master branch to return production to its stable state.

Updating a feature branch

First we'll update your local master branch. Go to your local project and check out the branch you want to merge into (your local master branch)

$ git checkout master

Fetch the remote, bringing the branches and their commits from the remote repository. You can use the -p, --prune option to delete any remote-tracking references that no longer exist in the remote. Commits to master will be stored in a local branch, remotes/origin/master.

@lh3
lh3 / fq_download.md
Last active December 30, 2017 22:22
Downloading gzip'd fastq
Source Dst. file type Protocol Time (s) Command Line
NCBI .sra ftp 296 wget
NCBI .fastq.gz sra toolkit ~23000 fastq-dump -Z --gzip --split-spot
local file sra=>fastq.gz sra toolkit ~15000 fastq-dump --gzip --split-spot --split-3
EBI .fastq.gz aspera 513+492 aspera -QT -l 300m
EBI .fastq.gz ftp 1876+1946 wget

Notes: