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so nlp fundamental awesome
1.advanced tokenization using nltk and regex
2.nltk tokenization
3.non ascii tokenization
4.charting with nltk
5.bag of words
6.buiding a counter with bag of words
7.preprocessing of texts
8.introductgion to gensim
9.tf-idf

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Serial Keys:

YV54A-2ZW5P-M887Y-UWXNE-QPUXD
VY3R2-0NW0L-H845Q-TDMXT-XQAT0
VC7JR-A0Z97-08EGZ-M4YNV-XVHD0
FC1TU-4RGEQ-084EP-2XQQX-ZGHWA
CU1WA-8HGEN-M815Z-HQP5E-QKADF
AY7D0-FTG44-H846Y-2XPGV-P32T8

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from keras.layers import Dense
from keras.models import Sequential



model=Sequential()
model.add(Dense(100))
model.add(Dense(50))
model.add(Dense(1))

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import tensorflow as tf

training_epochs = 500
n_neurons_in_h1 = 60
n_neurons_in_h2 = 60
learning_rate = 0.01

X = tf.placeholder(tf.float32, [None, n_features], name='features')
Y = tf.placeholder(tf.float32, [None, n_classes], name='labels')

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Full Stack Application

By the end of this exercise you will build a full stack application with a client, server, and database.

** NOTE ** Do not copy and paste. This project is about solidifying concepts and finding gaps in your knowledge. It is OK to look back at previous work, however, you should understand what every line does and be able to write from scratch.

Tasks

0. Plan

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F

#include <bits/stdc++.h>

using namespace std;

int main()
{
    long sum = 0;