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@mbednarski
mbednarski / w2v.ipynb
Created March 6, 2018 23:34
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@suhininalex
suhininalex / PageRank.kt
Created February 19, 2018 14:02
Page rank implementation: http://www.cs.princeton.edu/~chazelle/courses/BIB/pagerank.htm
class PageRank(val connections: Map<String, List<String>>) {
private val reversed: Map<String, List<String>> = connections
.flatMap { (k, v) -> v.map { it to k } }
.groupBy { it.first }
.mapValues { (_, v) -> v.map { it.second } }
private var pageRank: Map<String, Double> = connections.mapValues { (_, _) -> 0.0 }
private fun partialSum(point: String): Double {
@motiur
motiur / pytorch_keras_gcloud.txt
Last active June 20, 2018 09:04
Keras and Pytorch in Google Cloud VM
# This script is designed to work with ubuntu 16.04 LTS
# with keras 1.2.2 and the latest Pytorch with CUDA 8 support
##########################################################################
#This is used to install CUDA 8 driver for Tesla K80
##########################################################################
#!/bin/bash
echo "Checking for CUDA and installing."
# Check for CUDA and try to install.
if ! dpkg-query -W cuda-8-0; then
@Tushar-N
Tushar-N / pad_packed_demo.py
Last active October 27, 2024 15:17
How to use pad_packed_sequence in pytorch<1.1.0
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
seqs = ['gigantic_string','tiny_str','medium_str']
# make <pad> idx 0
vocab = ['<pad>'] + sorted(set(''.join(seqs)))
# make model
@ltfschoen
ltfschoen / proportions_of_missing_data_in_dataframe_columns.py
Last active October 20, 2021 07:43
Calculate percentage of NaN values in a Pandas Dataframe for each column. Exclude columns that do not contain any NaN values
# Author: Luke Schoen 2017
import pandas as pd
import numpy as np
import functools
# Create DataFrame
# df = pd.DataFrame(np.random.randn(10,2))
# Populate with NaN values
df = pd.DataFrame({'col1': ['1.111', '2.111', '3.111', '4.111'], 'col2': ['4.111', '5.111', np.NaN, '7.111'], 'col3': ['8', '9', np.NaN, np.NaN], 'col4': ['12', '13', '14', '15']})
@bartolsthoorn
bartolsthoorn / multilabel_example.py
Created April 29, 2017 12:13
Simple multi-laber classification example with Pytorch and MultiLabelSoftMarginLoss (https://en.wikipedia.org/wiki/Multi-label_classification)
import torch
import torch.nn as nn
import numpy as np
import torch.optim as optim
from torch.autograd import Variable
# (1, 0) => target labels 0+2
# (0, 1) => target labels 1
# (1, 1) => target labels 3
train = []
@kenzotakahashi
kenzotakahashi / pytorch_gru.py
Last active February 4, 2020 17:35
PyTorch GRU example with a Keras-like interface.
import numpy as np
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
from torch.autograd import Variable
np.random.seed(1337)
MAX_LEN = 30
@amirziai
amirziai / springboard xml.ipynb
Created February 26, 2017 01:04
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@karpathy
karpathy / pg-pong.py
Created May 30, 2016 22:50
Training a Neural Network ATARI Pong agent with Policy Gradients from raw pixels
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym
# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward
@a1ip
a1ip / 000_eoc_python_solutions.md
Last active May 2, 2019 07:48
Empire of code Python solutions https://empireofcode.com