BioSciEconomist

# *-----------------------------------------------------------------
# | PROGRAM NAME: ex matching.py
# | DATE: 6/25/21
# | CREATED BY: MATT BOGARD
# | PROJECT FILE:
# *----------------------------------------------------------------
# | PURPOSE: very basic matching and IPTW analysis with balance diagnostics
# *----------------------------------------------------------------

khyatimahendru

mrocklin

distributed:
  version: 2
  scheduler:
    bandwidth: 1000000000     # 100 MB/s estimated worker-worker bandwidth
  worker:
    memory:
      target: 0.90  # target fraction to stay below
      spill: False  # fraction at which we spill to disk
      pause: 0.80  # fraction at which we pause worker threads
      terminate: 0.95  # fraction at which we terminate the worker

planetceres

Setup of GPU Enabled VM for Distributed Tensorflow using Horovod

Update CentOS

Update CentOS and reboot (you will need to login again):

sudo yum update -y
sudo reboot

zthxxx

Note

For every body who using Win10/11 with new Office that you need to know:

to active Office without crack, just follow https://github.com/WindowsAddict/IDM-Activation-Script,

you wiil only need to run

irm https://massgrave.dev/ias | iex

JenZhao

Here are my solutions to all SQL problems on leetcode.

Rank

-- how to do rank with out window function
select t1.category, count(distinct t2.id) as dense_rank
from table t1, table t2
where t1.id = t2.id and t1.value <= t2.value 
group by t1.category

thoolihan

import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin

# Adapted from https://www.kaggle.com/ogrellier/python-target-encoding-for-categorical-features

class TargetEncoder(BaseEstimator, TransformerMixin):
    def __init__(self, columns, noise_level = 0):
        self.columns = columns
        self.maps = {}

kendricktan

"""
Dynamic Routing Between Capsules
https://arxiv.org/abs/1710.09829
"""

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.transforms as transforms

geffy

# -*- coding: utf-8 -*-
"""
Created on Mon Sep 23 23:16:44 2017

@author: Marios Michailidis

This is an example that performs stacking to improve mean squared error
This examples uses 2 bases learners (a linear regression and a random forest)
and  linear regression (again) as a meta learner to achieve the best score.
The initial train data are split in 2 halves to commence the stacking. 

aparrish