prof3ssorSt3v3

//Self-Signed Certificate for using with VS Code Live Server

//Save both files in a location you will remember

1. create a private key
openssl genrsa -aes256 -out localhost.key 2048
// you will be prompted to provide a password
//this will create localhost.key (call it whatever you like)

2. create the certificate

HarshTrivedi

import torch
from torch import LongTensor
from torch.nn import Embedding, LSTM
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium']
#
#     Step 1: Construct Vocabulary
#     Step 2: Load indexed data (list of instances, where each instance is list of character indices)

conormm

from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.utils import np_utils
import numpy as np

l1_nodes = 200
l2_nodes = 100
final_layer_nodes = 10

nzw0301

*2vec papers

1. act2vec, trace2vec, log2vec, model2vec https://link.springer.com/chapter/10.1007/978-3-319-98648-7_18
2. apk2vec https://arxiv.org/abs/1809.05693
3. app2vec http://paul.rutgers.edu/~qma/research/ma_app2vec.pdf
4. ast2vec https://arxiv.org/abs/2103.11614
5. attribute2vec https://arxiv.org/abs/2004.01375
6. author2vec http://dl.acm.org/citation.cfm?id=2889382
7. baller2vec https://arxiv.org/abs/2102.03291
8. bb2vec https://arxiv.org/abs/1809.09621

conormm

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs:

karpathy

"""
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)

rarylson

-- AppleScript to create a new file in Finder
--
-- Use it in Automator, with the following configuration:
-- - Service receives: no input
-- - In: Finder.app
--
-- References:
--  - http://apple.stackexchange.com/a/129702
--  - http://stackoverflow.com/a/6125252/2530295
--  - http://www.russellbeattie.com/blog/fun-with-the-os-x-finder-and-applescript

fappel

import java.lang.annotation.Retention;
import java.lang.annotation.RetentionPolicy;
import java.lang.annotation.Target;

@Retention( RetentionPolicy.RUNTIME )
@Target( {
  java.lang.annotation.ElementType.METHOD
} )
public @interface Repeat {
  public abstract int times();

kevin-smets

Default

Powerlevel10k

sarim

#!/bin/sh

function print_bar {
    GDONE=$1
    GPROG='['
    for i in $(seq 1 1 $GDONE)
    do
       GPROG=$GPROG'#'
    done