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| <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd"> | |
| <html> | |
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| <title>KDnuggets Weekly Digest</title> | |
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| <body style="background-color:#FFFFFF; font-family:arial,verdana,tahoma; font-size:14px;"> | |
| <table style="border:0px; border-collapse:collapse; width:680px; margin-left:auto; margin-right:auto;"> | |
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| <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd"> | |
| <html> | |
| <head> | |
| <title>KDnuggets Weekly Digest</title> | |
| </head> | |
| <body | |
| style=" | |
| background-color: #ffffff; | |
| font-family: arial, verdana, tahoma; | |
| font-size: 14px; |
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| <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd"> | |
| <html> | |
| <head> | |
| <title>KDnuggets Weekly Digest</title> | |
| </head> | |
| <body style="background-color:#FFFFFF; font-family:arial,verdana,tahoma; font-size:14px;"> | |
| <table style="border:0px; border-collapse:collapse; width:680px; margin-left:auto; margin-right:auto;"> | |
| <td> | |
| <!-- Header --> | |
| <table style="width:100%; border-collapse:collapse;"> |
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| from math import sqrt, floor | |
| import numpy as np | |
| def random(ds, k, random_state=42): | |
| """ | |
| Create random cluster centroids. | |
| Parameters | |
| ---------- |
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| from collections import Counter | |
| from string import punctuation | |
| from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS as stop_words | |
| import spacy | |
| def count_words(tokens): | |
| word_counts = {} | |
| for token in tokens: | |
| if token not in stop_words and token not in punctuation and token is not '\n': | |
| if token not in word_counts.keys(): |
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| class Vocabulary: | |
| PAD_token = 0 # Used for padding short sentences | |
| SOS_token = 1 # Start-of-sentence token | |
| EOS_token = 2 # End-of-sentence token | |
| def __init__(self, name): | |
| self.name = name | |
| self.word2index = {} | |
| self.word2count = {} | |
| self.index2word = {PAD_token: "PAD", SOS_token: "SOS", EOS_token: "EOS"} |
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| import os | |
| import speech_recognition as sr | |
| from pydub import AudioSegment | |
| from pydub.playback import play | |
| from gtts import gTTS as tts | |
| def speak(text): |
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| import numpy | |
| import GA | |
| """ | |
| The y=target is to maximize this equation ASAP: |
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| from keras import models | |
| from keras.layers import Dense, Dropout | |
| from keras.utils import to_categorical | |
| from keras.datasets import mnist | |
| from keras.utils.vis_utils import model_to_dot | |
| from IPython.display import SVG | |
| import livelossplot | |
| plot_losses = livelossplot.PlotLossesKeras() |
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