shantanuo

clf = Pipeline([("dct", DictVectorizer()), ("svc", LinearSVC())])
params = {
    "svc__C": [1e15, 1e13, 1e11, 1e9, 1e7, 1e5, 1e3, 1e1, 1e-1, 1e-3, 1e-5]
}
gs = GridSearchCV(clf, params, cv=10, verbose=2, n_jobs=-1)
gs.fit(X, y)
model = gs.best_estimator_

shantanuo

# downlaod links from dynamoDB
!aws dynamodb scan --table-name Movies --query "Items[*].[id.S,title.S]" --output json | sort -u > /tmp/download.txt

# copy github links and extract repo URLs
import pandas as pd
mylist = """
        "https://github.com/apoorvnandan/speech-recognition-primer"
        "https://github.com/asmitakulkarni/QuoteGenerator"
        "https://github.com/cjhutto/vaderSentiment"
        "https://github.com/docker/docker-bench-security"

shantanuo

import multiprocessing
import os
import requests

class MultiProcDownloader(object):
    def __init__(self, urls):
        self.urls = urls

    def run(self):
        jobs=[]

shantanuo

import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import label_ranking_average_precision_score
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from keras.utils import to_categorical

df = pd.read_json("../data/news_category_dataset.json", lines=True)

shantanuo

Response Headers
{"X-Amzn-Trace-Id":"Root=1-5d3fda3a-13e3be3a90a70be98b3be772;Sampled=0","Content-Type":"application/json"}



Logs
Execution log for request afd9f3f2-b28d-11e9-bb2a-4bb7e1da2e13
Tue Jul 30 05:48:42 UTC 2019 : Starting execution for request: afd9f3f2-b28d-11e9-bb2a-4bb7e1da2e13
Tue Jul 30 05:48:42 UTC 2019 : HTTP Method: POST, Resource Path: /

shantanuo

// Usage: mongo {Server without mongodb:// example 127.0.0.1:27017}/{DbName} [-u {Username}] [-p {Password}] < ./mongo-ls.js

var collections = db.getCollectionNames();

print('Collections inside the db:');
for(var i = 0; i < collections.length; i++){
  var name = collections[i];

  if(name.substr(0, 6) != 'system')
    print(name + ' - ' + db[name].count() + ' records');

shantanuo

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in 

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
from tensorflow.contrib.keras.api.keras.losses import binary_crossentropy
from collections import Counter

shantanuo

# output of npm start command...
# npm start

> amplify-js-app@1.0.0 start /amplify-js-app
> webpack && webpack-dev-server --mode development

Hash: 7ae7e983cf728aac0aca
Version: webpack 4.29.6
Time: 107ms
Built at: 03/14/2019 7:52:13 AM

shantanuo

import boto3
import datetime
import pandas as pd
import numpy as np

now = datetime.datetime.utcnow()
start = (now - datetime.timedelta(days=40)).strftime("%Y-%m-%d")
end = now.strftime("%Y-%m-%d")

cd = boto3.client("ce",

shantanuo

# https://towardsdatascience.com/transfer-learning-using-elmo-embedding-c4a7e415103c
import pandas as pd
import numpy as np
import re

import tensorflow_hub as hub
import tensorflow as tf
import keras
from tensorflow.python.keras.layers import Input, Dense, Lambda