im-noob

'''
  Reuire More than 12 GB ram to process all the request.. 
  !pip install nest-asyncio
'''

import asyncio
from aiohttp import ClientSession
import nest_asyncio
nest_asyncio.apply()

h3ssan

In some cases, only these lines will work

for product in IntelliJIdea WebStorm DataGrip PhpStorm CLion PyCharm GoLand RubyMine; do
    rm -rf ~/.config/$product*/eval 2> /dev/null
    rm -rf ~/.config/JetBrains/$product*/eval 2> /dev/null
done

But if not, try these

byelipk

Exercises

1. What Linear Regression training algorithm can you use if you have a training set with millions of features?

You could use batch gradient descent, stochastic gradient descent, or mini-batch gradient descent. SGD and MBGD would work the best because neither of them need to load the entire dataset into memory in order to take 1 step of gradient descent. Batch would be ok with the caveat that you have enough memory to load all the data.

The normal equations method would not be a good choice because it is computationally inefficient. The main cause of the computational complexity comes from inverse operation on an (n x n) matrix.

O n2 . 4 to O n3

2. Suppose the features in your training set have very different scales: what algorithms might suffer from this, and how? What can you do about it?