b01

#!/bin/sh

# Copyright 2023 Khalifah K. Shabazz
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the “Software”),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:

cyyyu

LEETCODE_USER=leetcode username
GITHUB_TOKEN=github token
GIST_ID=gist id
GIST_FILE=progress.txt

ringsaturn

nlg-eval on zh-CN

Issue #54

That will be easy if you use something like jieba. Example:

import jieba

# text from http://li-xirong.github.io/pub/icmr2016_chinese_caption.pdf

jingriver

https://hackernoon.com/top-10-system-design-interview-questions-for-software-engineers-8561290f0444
https://medium.com/@codingfreak/binary-tree-interview-questions-and-practice-problems-439df7e5ea1f
https://cspiration.com/leetcodeClassification

heap sort
https://www.hackerearth.com/practice/algorithms/sorting/heap-sort/tutorial/

https://medium.com/@randerson112358/lets-build-a-min-heap-4d863cac6521

https://stackblitz.com/

t-vi

from typing import List, Tuple, Optional, Union, Any, ContextManager, Callable, overload

import builtins
import math
import pickle


class dtype: ...
_dtype = dtype

BIGBALLON

#!/bin/bash
#
# script to extract ImageNet dataset
# ILSVRC2012_img_train.tar (about 138 GB)
# ILSVRC2012_img_val.tar (about 6.3 GB)
# make sure ILSVRC2012_img_train.tar & ILSVRC2012_img_val.tar in your current directory
#
#  https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md
# 
#  train/

tgsmith61591

# -*- coding: utf-8 -*-
#
# Author: Taylor G Smith
#
# Recommender system ranking metrics derived from Spark source for use with
# Python-based recommender libraries (i.e., implicit, 
# http://github.com/benfred/implicit/). These metrics are derived from the
# original Spark Scala source code for recommender metrics.
# https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala

jeasinema

#!/usr/bin/env python
# -*- coding:UTF-8 -*-

import torch
import torch.nn as nn
import torch.nn.init as init


def weight_init(m):
    '''

rodricels

# My tmux configuration, partly based on https://github.com/wbkang/wbk-stow/blob/master/tmux-config/.tmux.conf

# Scroll History
set -g history-limit 50000

# show messages for 4 seconds instead
set -g display-time 4000

# set first window to index 1 (not 0) to map more to the keyboard layout
set-option -g renumber-windows on

MInner

import datetime
import linecache
import os

import pynvml3
import torch

print_tensor_sizes = True
last_tensor_sizes = set()
gpu_profile_fn = f'{datetime.datetime.now():%d-%b-%y-%H:%M:%S}-gpu_mem_prof.txt'