March 2018
Intermediate to advanced
484 pages
10h 31m
English
book
Deep Learning with TensorFlow - Second Edition
by Giancarlo Zaccone, Vihan Jain, Md. Rezaul Karim, Motaz Saad
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Implementing an RNN for spam prediction
In this section, we will see how to implement an RNN in TensorFlow to predict spam/ham from texts.
Data description and preprocessing
The popular spam dataset from the UCI ML repository will be used, which can be downloaded from http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smssp amcollection.zip.
The dataset contains texts from several emails, some of which were marked as spam. Here we will train a model that will learn to distinguish between spam and non-spam emails using only the text of the email. Let's get started by importing the required libraries and model:
import os import re import io import requests import numpy as np import matplotlib.pyplot as plt import tensorflow as tf from zipfile ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.